Predicting the Solar Resource and Power Load

Size: px
Start display at page:

Download "Predicting the Solar Resource and Power Load"

Transcription

1 1 Predicting the Solar Resource and Power Load David Sehloff, Celso Torres Supervisor: Alex Cassidy, Dr. Arye Nehorai Department of Electrical and Systems Engineering Washington University in St. Louis Spring 2015 Abstract The value of solar energy is recognized for a variety of reasons, such as environmental and public health and security of supply. Inherently associated with these sources, however, is a degree of uncertainty. Knowing a day in advance how much power renewable sources will deliver, along with how much power will be demanded, would help suppliers determine the type and quantities of other resources to use. The goal of this research is to build predictors for both the power load and the solar resource using the machine learning technique known as support vector regression. Given weather, solar, and power load data from several years, the support vector algorithm builds a predictor model that outputs day-ahead predicted power load and solar resource based on weather forecasts. This allows analysis of the net power demands from the other sources of electric power for various levels of penetration of photovoltaic installations. The results of this analysis could be used by utilities or balancing authorities to plan which power plants should run at certain times and when energy storage or demand shifting incentives should be utilized. Figure 1. The net load, or power load minus wind and solar generation, for the state of California on March 31. I. INTRODUCTION The electric power grid that we know and rely on every day is an engineering feat. Many challenges had to be overcome to reliably provide electric power whenever there is demand for it. Providing this electric power was even called "the greatest engineering achievement of the 20th Century" by the National Academy of Engineering. But the grid is facing new challenges in the 21 st century. We can see the value of renewable energy sources in many areas, such as environmental and public health and security of energy supply. In addition, their operation costs can be extremely small. Inherently associated with these sources, however, is a degree of uncertainty. Unlike coal, nuclear, or natural gas power plants, operators cannot directly control the power that renewable sources output. It would be advantageous to know in advance how much power will be delivered by renewable sources, along with how much power consumers will demand, so that suppliers know when other resources should be used. An example from the California Independent System Operator shows what the changing load profile might look like. Due to an increasing presence of solar resources connected to the grid, the power required from other sources will significantly drop during the peak solar generation hours in the middle of the day. It will then steeply increase as the sun sets, lights turn on, and people come home to their kitchens, laundry, and other appliances and electronics. The risk of generating too much power at the bottom of the curve and not generating enough at the sharp increase grows with the uncertainty of renewable sources. Figure 1, from the California ISO, shows an example of the actual power demand on March 31 minus the power from solar and wind resources. Two curves of actual measurements are shown, and the rest are projections based on the expected increase in solar and wind installations. This graph, often called a "Duck Curve" in the industry because of its shape, helps depict how the solar resource affects the power grid and the effects that uncertainty can have. When a power grid has a large

2 2 amount of solar power capacity, the middle of this curve can fluctuate dramatically day-to-day. [1] Accurate forecasting of the hourly solar resource, along with the hourly power demand, one day in advance would help with power grid planning and decrease the costs associated with the integration of solar power with its inherent uncertainty. Our goal is to build predictors of the solar resource and power load profile that will reduce this uncertainty. II. BUILDING THE PREDICTOR MODELS To make these predictions, we learned about the theory and implementation of machine learning, and we focused on Support Vector Machines. This type of learning algorithm was first developed in the 1960s and became the subject of much research in the 1990s. Vapnik and colleagues, including Chervonenkis, Schölkopf, and Smola were behind these foundational developments. The algorithm, which has quickly become ubiquitous in diverse practical applications, is named for support vectors, or the data points that are used to build the model. [2] Support vector machines, or SVMs, can be used for either classification or regression. Classification groups each instance into a certain class based on its attributes, and it uses as support vectors only the data points that are close to the classification boundary. Regression, on the other hand, uses the attributes to fit a function to the data, and it uses as support vectors only the points that are greater than a specified distance from this function. [2],[3] In this project, we use support vector regression to build our solar resource and power demand predictors. The support vector regression algorithm is an optimization problem in which we want to choose a hyperplane through the data which is close to as many points as possible, such that these points are within a certain specified distance of ε from the hyperplane. It also is desired that this hyperplane is relatively flat. This leads to the minimization of a function with two terms: one describing the sum of the distances by which each point is outside of the specified ε-insensitive band, and a second term which characterizes the flatness of the function by the squared norm of w, the hyperplane s normal. This quadratic optimization is subject to the accuracy constraints that each point is within the ε band plus or minus slack variables ξ or ξ*, to allow for points to be outside of the range. These ξ and ξ* slack variables make up the first term in the objective function. This term is multiplied by a cost parameter, C, which can be tuned to, in effect, specify the balance of the model s accuracy versus simplicity. A large value puts an emphasis on including points within the ε band, while a small value emphasizes more having a flatter, simpler hyperplane to characterize the data. Below is Vapnik s 1995 formulation of the problem, which can be solved by formulating its dual. For this, a kernel trick is used, which maps the data to a higher dimension to find a solution. A common kernel, and the one which we used, is the radial basis function. It has an adjustable parameter, γ. [2]-[4] We can change the fit of the model to the data set by varying the parameters ε, C, and γ. According to Müller et al., these parameters, though powerful means for regularization and adaptation to the noise in the data, are difficult to select. The lack of a simple, general method or theoretical bounds for their selection shows that this is an area of support vector machines with potential for growth. [3] In our project, we used a simple grid search algorithm which built models by varying C and γ through a specified range and compared the error of each model in a cross-validation. Parameter selection aims to achieve the balance between over- and under-fitting. Under-fitting the data results in a model that does not capture all the characteristics of the set, while over-fitting builds a model that captures the individual peculiarities or noise of the given data so well that it is not useful for predicting future behavior. In our tests for power load profile parameters, the best results came from C=1024 and γ=7. For the solar resource, we found C=256 and γ=4 to perform well.

3 3 Support Vector Regression can be implemented in several ways. A common package which is available for use in MATLAB and Java is called LibSVM. We implemented it in both MATLAB directly and in Java through a programming interface called Weka, which organizes the training and testing process and gives the capability to filter the data, such as normalizing the attributes, which is important to ensure that each is given equal weight. In the preliminary stages of experimenting with regression techniques, we worked with several data sets, including detailed weather information readily available from the National Renewable Energy Laboratory for a typical meteorological year at many locations around the country and historical hourly data from a network of weather stations in the northwest U.S. available from the Bureau of Reclamation. The latter source of data proved to be the most useful, as it is continuously updated and includes many attributes that are important for predicting solar radiation. Detailed power load information from southern Washington, northern Oregon, and western Idaho was also available from the Bonneville Power Administration Balancing Authority. For making predictions, we obtained forecasted weather attributes from the National Weather Service. To build a predictor for solar radiation, we obtained hourly weather data from January 2010 through March The attributes in this data were the day of the year, time, temperature, relative humidity, wind gust speed, and wind speed. The target value was the global horizontal irradiance, or GHI, which describes the solar radiation incident on a flat photovoltaic panel. We took data from weather stations at five locations: Imbler, Powell Butte, Echo, and Baker Valley in Oregon, and George in Washington. These locations are shown by the red markers in Figure 2. The solar radiation showed notable variance across locations and hour-to-hour in each location. At a glance, it seemed that this variance was greater than that of the sunniest region of the country, southern California and Arizona, which suggests that a reliable solar predictor could be important in an area such as this one. Figure 2. The weather stations. The red markers indicate locations for solar forecasting, and the blue markers indicate temperature data sources for power load forecasting. To build the power load predictor, we used hourly weather and power load data from January 2007 to March The power load does not correlate strongly with as many features as the solar radiation. Successful peak-load predictors have been built using only date and time as attributes. [4] For this project s aim to predict the hourly load, we found it beneficial to include temperature data from two locations: Hood River, OR, and Boise, ID, shown in teal in Figure 2. It is important to note that the power load generally follows a slightly different pattern on weekends. We included a binary attribute that describes whether or not the day is a weekend. In addition, the temperature tends to have the opposite effect on the power load depending on whether the date is between roughly the beginning of April and the end of September. [4] We added a binary attribute describing this. The other attributes were the year, day, time, temperature at Hood River, and temperature at Boise. The target value was the power load in MW. III. TESTING AND VERIFICATION After building the predictor models for power load and solar radiation at each location, we performed the first predictions using as inputs historical weather data for five days in April, We then compared the predictions of the model to the actual measurements of power load and solar radiation. For these tests, which do not depend on weather forecasts, we could test our model on as many days as were available from the weather database. Note that we did not test our models on any data points that

4 Solar Radiation (W/m 2 ) Power Load (MW) 4 we used for training. Figure 3 shows our test of the power load predictor on the five days in April 2015 shown on the horizontal axis by the day number (from 1 to 365). Figure 4 shows our test of the solar predictor on the same days. We processed the solar model s output to ensure plausible predictions: any value that was negative was set to zero, and all values between 10:00 p.m. and 3:00 a.m. were set to zero Test of Load Predictor Using Observed Weather Actual 4500 Predicted Day Figure 3. Test of the load predictor, with observed weather from Hood River, OR and Boise, ID as inputs. The actual values are also shown. The Mean Absolute Percent Error is 3.96% Test of Solar Predictor Using Observed Weather Actual Predicted Day Figure 4. Test of the solar predictor, with observed weather from Baker Valley, OR as inputs. The actual values are also shown. The Mean Absolute Error is W/m 2. Error in load forecasts is most commonly characterized by the Mean Absolute Percent Error (MAPE). This is defined as the mean of the absolute difference between the actual and forecasted values at each point, divided by the actual value at that point, shown by the expression below. Error for the solar prediction is more difficult to characterize in a way that can be easily compared across data sets. Many of the values are zero, and the percent error cannot be well determined for those cases. We characterize the error as Mean Absolute Error (MAE), defined as the mean of the absolute difference between real and forecasted values at each point, as shown below.

5 Solar Radiation (W/m 2 ) Power Load (MW) 5 The load predictor had a MAPE of 3.96%, and the solar predictor had a MAE of W/m 2 in these tests. Next, we input forecasted weather data for April 12 th to 14 th to our models to create 48-hour-ahead predictions of the solar radiation in each of the five locations and the power load for the overall area. Figure 5 shows the load prediction, which has a MAPE of 4.12%. Figure 6 shows the best solar forecast, for Imbler, OR, which has a MAE of W/m 2. Table 1 shows the MAE for each of the five locations Hour Load Forecast Actual Predicted Date Figure 5. Output of the load predictor using forecasted temperature as an input. The actual values are also shown. The Mean Absolute Percent Error is 4.12% Hour Solar Forecast for Imbler, OR Actual Predicted Date Figure 6. Output of the solar predictor using forecasted weather as inputs. The actual values are also shown. The Mean Absolute Error is W/m 2.

6 Power (MW) 6 Note: The days of Figures 5 and 6, the 48-hour forecasts, and of Figures 3 and 4, the tests on historical data, overlap because the 48-hour weather forecast data was collected before the historical data tests were performed; the 48-hour forecasts used all forecasted data and represent the prediction results that could be expected up to 48 hours in advance. Location MAE Imbler, OR Powell Butte, OR Echo, OR Baker Valley, OR George, WA These forecasting results can be applied to predict the change in demand from conventional generation sources when power grid has a certain capacity of solar installations. In our case, the chosen locations do not have large photovoltaic plants, but we can see an effect of our results if we model plants of a certain capacity at the locations of our predictors. For this, we assume that the power output of the plant varies linearly with incident solar radiation, neglecting effects of temperature and other factors on photovoltaic cell efficiency. For example, if a 30 MW-capacity photovoltaic plant were installed in each of the five locations, the approximate total power that these five plants would produce is shown in Figure Total Power Output from Five 30MW Installations Table 1. Error in solar prediction at each of the five locations, characterized by MAE Calculated Output Forecasted Output Date and Time Figure 7. Predicted total output modeling one 30MW photovoltaic plant at each forecast location compared to the calculated output based on actual solar radiation. Subtracting this generation from the predicted load for the same time period gives the predicted net load. Comparing the predicted load to the predicted net load, as in Figure 8., shows the predicted effect that the solar resource will have on the power grid. We can compare this with the actual net load, which comes from actual load minus the calculated solar output. The mean absolute percent deviation of the prediction from the calculated net load is 4.15%. It is clear that this deviation is largely from the power load; the solar resource has a small effect on this value since the modeled capacity is a small fraction of the load. The analysis could easily be done for additional locations or a larger installation to see a more dramatic impact on the grid. Ultimately, this method is to be applied for a certain installed capacity, and its impacts will be larger for larger capacities of solar generation.

7 Load (MW) Forecasted Electric Power Load Forecasted Net Load with Solar Forecasted Load without Solar /12 12:00 4/12 18:00 4/13 0:00 4/13 6:00 4/13 12:00 4/13 18:00 Date 4/14 0:00 4/14 6:00 4/14 12:00 Figure 8. Comparison of the load with and without solar generation. A peak capacity of 150MW has a small effect. The deviation from the calculations using actual load and radiation is 4.15%. Actual values are not shown. IV. DISCUSSION & CONCLUSIONS Our results show that support vector regression can be a viable means to solve the forecasting problems presented. We have found several important factors for successful power load and solar resource forecasts. The first is to include a large amount of data for training the model. By including five to eight years of hourly data, our models were able to capture the periodic behavior based on the hour of the day and the day of the year. We performed tests with smaller data sets of several months to several years, but the largest data sets gave the best results. With large data sets, it is also important to have a procedure for missing values. The algorithm we used interprets missing values as zero, which would falsely represent data from most of our attributes. Since fewer than about 0.1% of our data points had missing values, we deleted all such points from the training sets. Another important factor is the selection of parameters. Certain choices of C and γ gave us predictions that were rather flat, staying near the average value and not reaching the minima or maxima, while others varied widely, at times predicting a dramatic change where the actual value remained nearly constant. These are examples of under- and over-fitting. We used the parameters that we found to give the best balance between these. Much improvement could be made to our results with improved methods of parameter selection for C and. We used a grid search method, which is time consuming and can be inaccurate. Parameter selection is an area of ongoing research in machine learning, and a variety of methods such as particle swarm optimization have been shown to lead to improved selection of parameters. [5] With more research in these methods and additional computing power, our models could be made more robust and give more accurate predictions. In addition, with more time and computing power, we could incorporate more years of data into our model building. We found that adding several years of data improved the accuracy of our results, but the time required for computing with any additional data quickly became prohibitive. Another improvement could come in the data that we use as our attributes. More sophisticated weather measurements such as percent sky cover or even sky images could be used as attributes. In-depth study of the dynamics of cloud behavior could lead to a better understanding of the changes in solar radiation and an improved model for prediction. Weather stations could be built

8 8 to take the measurements that are found to be most important. It would also be beneficial to obtain data for the actual power output of a photovoltaic installation. This data would be more useful than the incident radiation as the target value of the prediction. Finally, in addition to improving the performance of support vector regression models, it would be worthwhile to investigate other methods of machine learning for this problem. One option, if several working models were built, would be to combine them into a hybrid model, as Wu et al. showed very successfully for photovoltaic output. [7] A combination of these improvements applied at a specific location could result in accurate prediction of the power that a photovoltaic installation at that specific location will generate. This information would be valuable for the sale of the power or the planning of grid operations, allowing better utilization of power plants, from base- to peak-load, along with storage and demand response, ultimately reducing the cost of harnessing energy from the sun. ACKNOWLEDGMENT We would like to thank Alex Cassidy and Dr. Arye Nehorai for their support and encouragement and Professor Ed Richter for his coordination of the undergraduate research. REFERENCES [1] The Duck Curve: Managing a Green Grid." Flexible Resources Help Renewables. California ISO, Web. 20 Feb [2] Smola, Alex J., and Bernhard Schölkopf. "A Tutorial on Support Vector Regression." Statistics and Computing 14.3 (2004): Web. 22 Jan [3] K.-R. Müller, A. J. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik, Predicting time series with support vector machines, in Artificial Neural Networks ICANN 97, vol. 1327, W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, Eds. Springer Berlin Heidelberg, 1997, pp [4] Bo-Juen Chen, Ming-Wei Chang, and Chih-Jen Lin, Load forecasting using support vector Machines: a study on EUNITE competition 2001, Power Systems, IEEE Transactions on, vol. 19, no. 4, pp , Nov [5] Hong, Wei-Chiang. "Chaotic Particle Swarm Optimization Algorithm in a Support Vector Regression Electric Load Forecasting Model." Energy Conversion and Management 50.1 (2009): [6] Rojas, I., O. Valenzuela, F. Rojas, A. Guillen, L.j. Herrera, H. Pomares, L. Marquez, and M. Pasadas. "Soft-computing Techniques and ARMA Model for Time Series Prediction." Neurocomputing (2008): [7] Yuan-Kang Wu, Chao-Rong Chen, and Hasimah Abdul Rahman, A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation, International Journal of Photoenergy, vol. 2014, Article ID , 9 pages, [8] Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten (2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1. [9] Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1- -27:27, Software available at DATA "AgriMet Historical Hourly (Dayfile) Data Access -- Bureau of Reclamation." AgriMet Historical Hourly (Dayfile) Data Access -- Bureau of Reclamation. Web. 14 Apr "WIND GENERATION & Total Load in The BPA Balancing Authority." BPA: Balancing Authority Load & Total Wind Generation. Web. 14 Apr "Weather Forecasts." National Forecast Maps. National Weather Service, 12 Apr Web. 12 Apr <

Comparing the Results of Support Vector Machines with Traditional Data Mining Algorithms

Comparing the Results of Support Vector Machines with Traditional Data Mining Algorithms Comparing the Results of Support Vector Machines with Traditional Data Mining Algorithms Scott Pion and Lutz Hamel Abstract This paper presents the results of a series of analyses performed on direct mail

More information

Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and Numerical Weather Prediction

Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and Numerical Weather Prediction Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and Numerical Weather Prediction Jin Xu, Shinjae Yoo, Dantong Yu, Dong Huang, John Heiser, Paul Kalb Solar Energy Abundant, clean, and secure

More information

Introduction to Support Vector Machines. Colin Campbell, Bristol University

Introduction to Support Vector Machines. Colin Campbell, Bristol University Introduction to Support Vector Machines Colin Campbell, Bristol University 1 Outline of talk. Part 1. An Introduction to SVMs 1.1. SVMs for binary classification. 1.2. Soft margins and multi-class classification.

More information

How To Forecast Solar Power

How To Forecast Solar Power Forecasting Solar Power with Adaptive Models A Pilot Study Dr. James W. Hall 1. Introduction Expanding the use of renewable energy sources, primarily wind and solar, has become a US national priority.

More information

Data Quality Mining: Employing Classifiers for Assuring consistent Datasets

Data Quality Mining: Employing Classifiers for Assuring consistent Datasets Data Quality Mining: Employing Classifiers for Assuring consistent Datasets Fabian Grüning Carl von Ossietzky Universität Oldenburg, Germany, fabian.gruening@informatik.uni-oldenburg.de Abstract: Independent

More information

Getting Even More Out of Ensemble Selection

Getting Even More Out of Ensemble Selection Getting Even More Out of Ensemble Selection Quan Sun Department of Computer Science The University of Waikato Hamilton, New Zealand qs12@cs.waikato.ac.nz ABSTRACT Ensemble Selection uses forward stepwise

More information

Predicting Solar Generation from Weather Forecasts Using Machine Learning

Predicting Solar Generation from Weather Forecasts Using Machine Learning Predicting Solar Generation from Weather Forecasts Using Machine Learning Navin Sharma, Pranshu Sharma, David Irwin, and Prashant Shenoy Department of Computer Science University of Massachusetts Amherst

More information

VOLATILITY AND DEVIATION OF DISTRIBUTED SOLAR

VOLATILITY AND DEVIATION OF DISTRIBUTED SOLAR VOLATILITY AND DEVIATION OF DISTRIBUTED SOLAR Andrew Goldstein Yale University 68 High Street New Haven, CT 06511 andrew.goldstein@yale.edu Alexander Thornton Shawn Kerrigan Locus Energy 657 Mission St.

More information

A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources

A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources Aris-Athanasios Panagopoulos1 Joint work with Georgios Chalkiadakis2 and Eftichios Koutroulis2 ( Predicting the

More information

INTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr.

INTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr. INTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr. Meisenbach M. Hable G. Winkler P. Meier Technology, Laboratory

More information

Statistical Learning for Short-Term Photovoltaic Power Predictions

Statistical Learning for Short-Term Photovoltaic Power Predictions Statistical Learning for Short-Term Photovoltaic Power Predictions Björn Wolff 1, Elke Lorenz 2, Oliver Kramer 1 1 Department of Computing Science 2 Institute of Physics, Energy and Semiconductor Research

More information

Impact of Reflectors on Solar Energy Systems

Impact of Reflectors on Solar Energy Systems Impact of Reflectors on Solar Energy Systems J. Rizk, and M. H. Nagrial Abstract The paper aims to show that implementing different types of reflectors in solar energy systems, will dramatically improve

More information

EVALUATING SOLAR ENERGY PLANTS TO SUPPORT INVESTMENT DECISIONS

EVALUATING SOLAR ENERGY PLANTS TO SUPPORT INVESTMENT DECISIONS EVALUATING SOLAR ENERGY PLANTS TO SUPPORT INVESTMENT DECISIONS Author Marie Schnitzer Director of Solar Services Published for AWS Truewind October 2009 Republished for AWS Truepower: AWS Truepower, LLC

More information

Making Sense of the Mayhem: Machine Learning and March Madness

Making Sense of the Mayhem: Machine Learning and March Madness Making Sense of the Mayhem: Machine Learning and March Madness Alex Tran and Adam Ginzberg Stanford University atran3@stanford.edu ginzberg@stanford.edu I. Introduction III. Model The goal of our research

More information

STEADYSUN THEnergy white paper. Energy Generation Forecasting in Solar-Diesel-Hybrid Applications

STEADYSUN THEnergy white paper. Energy Generation Forecasting in Solar-Diesel-Hybrid Applications STEADYSUN THEnergy white paper Energy Generation Forecasting in Solar-Diesel-Hybrid Applications April 2016 Content 1 Introduction... 3 2 Weather forecasting for solar-diesel hybrid systems... 4 2.1 The

More information

Predicting daily incoming solar energy from weather data

Predicting daily incoming solar energy from weather data Predicting daily incoming solar energy from weather data ROMAIN JUBAN, PATRICK QUACH Stanford University - CS229 Machine Learning December 12, 2013 Being able to accurately predict the solar power hitting

More information

CHARACTERISTICS IN FLIGHT DATA ESTIMATION WITH LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINES

CHARACTERISTICS IN FLIGHT DATA ESTIMATION WITH LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINES CHARACTERISTICS IN FLIGHT DATA ESTIMATION WITH LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINES Claus Gwiggner, Ecole Polytechnique, LIX, Palaiseau, France Gert Lanckriet, University of Berkeley, EECS,

More information

Studying Auto Insurance Data

Studying Auto Insurance Data Studying Auto Insurance Data Ashutosh Nandeshwar February 23, 2010 1 Introduction To study auto insurance data using traditional and non-traditional tools, I downloaded a well-studied data from http://www.statsci.org/data/general/motorins.

More information

REDUCING UNCERTAINTY IN SOLAR ENERGY ESTIMATES

REDUCING UNCERTAINTY IN SOLAR ENERGY ESTIMATES REDUCING UNCERTAINTY IN SOLAR ENERGY ESTIMATES Mitigating Energy Risk through On-Site Monitoring Marie Schnitzer, Vice President of Consulting Services Christopher Thuman, Senior Meteorologist Peter Johnson,

More information

A Study on the Comparison of Electricity Forecasting Models: Korea and China

A Study on the Comparison of Electricity Forecasting Models: Korea and China Communications for Statistical Applications and Methods 2015, Vol. 22, No. 6, 675 683 DOI: http://dx.doi.org/10.5351/csam.2015.22.6.675 Print ISSN 2287-7843 / Online ISSN 2383-4757 A Study on the Comparison

More information

Solar Variability and Forecasting

Solar Variability and Forecasting Solar Variability and Forecasting Jan Kleissl, Chi Chow, Matt Lave, Patrick Mathiesen, Anders Nottrott, Bryan Urquhart Mechanical & Environmental Engineering, UC San Diego http://solar.ucsd.edu Variability

More information

HERBERT T. HAYDEN SOLAR TECHNOLOGY COORDINATOR FOR ARIZONA PUBLIC SERVICE COMPANY PHOENIX ARIZONA

HERBERT T. HAYDEN SOLAR TECHNOLOGY COORDINATOR FOR ARIZONA PUBLIC SERVICE COMPANY PHOENIX ARIZONA HERBERT T. HAYDEN SOLAR TECHNOLOGY COORDINATOR FOR ARIZONA PUBLIC SERVICE COMPANY PHOENIX ARIZONA BEFORE THE US HOUSE OF REPRESENTATIVES SUBCOMMITTEE ON ENERGY AND ENVIRONMENT, HOUSE COMMITTEE ON SCIENCE

More information

SOLAR IRRADIANCE FORECASTING, BENCHMARKING of DIFFERENT TECHNIQUES and APPLICATIONS of ENERGY METEOROLOGY

SOLAR IRRADIANCE FORECASTING, BENCHMARKING of DIFFERENT TECHNIQUES and APPLICATIONS of ENERGY METEOROLOGY SOLAR IRRADIANCE FORECASTING, BENCHMARKING of DIFFERENT TECHNIQUES and APPLICATIONS of ENERGY METEOROLOGY Wolfgang Traunmüller 1 * and Gerald Steinmaurer 2 1 BLUE SKY Wetteranalysen, 4800 Attnang-Puchheim,

More information

A Regression Approach for Forecasting Vendor Revenue in Telecommunication Industries

A Regression Approach for Forecasting Vendor Revenue in Telecommunication Industries A Regression Approach for Forecasting Vendor Revenue in Telecommunication Industries Aida Mustapha *1, Farhana M. Fadzil #2 * Faculty of Computer Science and Information Technology, Universiti Tun Hussein

More information

Support Vector Machine. Tutorial. (and Statistical Learning Theory)

Support Vector Machine. Tutorial. (and Statistical Learning Theory) Support Vector Machine (and Statistical Learning Theory) Tutorial Jason Weston NEC Labs America 4 Independence Way, Princeton, USA. jasonw@nec-labs.com 1 Support Vector Machines: history SVMs introduced

More information

Data Mining - Evaluation of Classifiers

Data Mining - Evaluation of Classifiers Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010

More information

Modeling of System of Systems via Data Analytics Case for Big Data in SoS 1

Modeling of System of Systems via Data Analytics Case for Big Data in SoS 1 Modeling of System of Systems via Data Analytics Case for Big Data in SoS 1 Barnabas K. Tannahill Aerospace Electronics and Information Technology Division Southwest Research Institute San Antonio, TX,

More information

Power Prediction Analysis using Artificial Neural Network in MS Excel

Power Prediction Analysis using Artificial Neural Network in MS Excel Power Prediction Analysis using Artificial Neural Network in MS Excel NURHASHINMAH MAHAMAD, MUHAMAD KAMAL B. MOHAMMED AMIN Electronic System Engineering Department Malaysia Japan International Institute

More information

Auburn University s Solar Photovoltaic Array Tilt Angle and Tracking Performance Experiment

Auburn University s Solar Photovoltaic Array Tilt Angle and Tracking Performance Experiment Auburn University s Solar Photovoltaic Array Tilt Angle and Tracking Performance Experiment Julie A. Rodiek 1, Steve R. Best 2, and Casey Still 3 Space Research Institute, Auburn University, AL, 36849,

More information

Solar Power at Vernier Software & Technology

Solar Power at Vernier Software & Technology Solar Power at Vernier Software & Technology Having an eco-friendly business is important to Vernier. Towards that end, we have recently completed a two-phase project to add solar panels to our building

More information

MAXIMIZING RETURN ON DIRECT MARKETING CAMPAIGNS

MAXIMIZING RETURN ON DIRECT MARKETING CAMPAIGNS MAXIMIZING RETURN ON DIRET MARKETING AMPAIGNS IN OMMERIAL BANKING S 229 Project: Final Report Oleksandra Onosova INTRODUTION Recent innovations in cloud computing and unified communications have made a

More information

Effects of PV Electricity Generation on Wholesale Power Prices Summary 2012 and January 2013

Effects of PV Electricity Generation on Wholesale Power Prices Summary 2012 and January 2013 Effects of PV Electricity Generation on Wholesale Power Prices Summary 2012 and January 2013 3 Embarcadero Center, Suite 2360 San Francisco, CA 94111 USA tel: 415.692.7730 research@renewableanalytics.com

More information

Evaluation of Machine Learning Techniques for Green Energy Prediction

Evaluation of Machine Learning Techniques for Green Energy Prediction arxiv:1406.3726v1 [cs.lg] 14 Jun 2014 Evaluation of Machine Learning Techniques for Green Energy Prediction 1 Objective Ankur Sahai University of Mainz, Germany We evaluate Machine Learning techniques

More information

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015 An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content

More information

A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning.

A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning. A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning. 31st Annual International Symposium on Forecasting Lourdes Ramírez Santigosa Martín

More information

An Introduction to Data Mining

An Introduction to Data Mining An Introduction to Intel Beijing wei.heng@intel.com January 17, 2014 Outline 1 DW Overview What is Notable Application of Conference, Software and Applications Major Process in 2 Major Tasks in Detail

More information

Overview of BNL s Solar Energy Research Plans. March 2011

Overview of BNL s Solar Energy Research Plans. March 2011 Overview of BNL s Solar Energy Research Plans March 2011 Why Solar Energy Research at BNL? BNL s capabilities can advance solar energy In the Northeast World class facilities History of successful research

More information

In this chapter, you will learn improvement curve concepts and their application to cost and price analysis.

In this chapter, you will learn improvement curve concepts and their application to cost and price analysis. 7.0 - Chapter Introduction In this chapter, you will learn improvement curve concepts and their application to cost and price analysis. Basic Improvement Curve Concept. You may have learned about improvement

More information

An Analysis of Siting Opportunities for Concentrating Solar Power Plants in the Southwestern United States

An Analysis of Siting Opportunities for Concentrating Solar Power Plants in the Southwestern United States An of Siting Opportunities for Concentrating Solar Power Plants in the Southwestern United States Mark S. Mehos National Renewable Energy Laboratory Golden, Colorado Phone: 303-384-7458 Email: mark_mehos@nrel.gov

More information

Renewable Energy. Solar Power. Courseware Sample 86352-F0

Renewable Energy. Solar Power. Courseware Sample 86352-F0 Renewable Energy Solar Power Courseware Sample 86352-F0 A RENEWABLE ENERGY SOLAR POWER Courseware Sample by the staff of Lab-Volt Ltd. Copyright 2009 Lab-Volt Ltd. All rights reserved. No part of this

More information

Integrating Renewable Electricity on the Grid. A Report by the APS Panel on Public Affairs

Integrating Renewable Electricity on the Grid. A Report by the APS Panel on Public Affairs Integrating Renewable Electricity on the Grid A Report by the APS Panel on Public Affairs 2 Integrating Renewable Electricity on the Grid Executive Summary The United States has ample renewable energy

More information

An Autonomous Agent for Supply Chain Management

An Autonomous Agent for Supply Chain Management In Gedas Adomavicius and Alok Gupta, editors, Handbooks in Information Systems Series: Business Computing, Emerald Group, 2009. An Autonomous Agent for Supply Chain Management David Pardoe, Peter Stone

More information

How To Predict Web Site Visits

How To Predict Web Site Visits Web Site Visit Forecasting Using Data Mining Techniques Chandana Napagoda Abstract: Data mining is a technique which is used for identifying relationships between various large amounts of data in many

More information

Big Data Analytic Paradigms -From PCA to Deep Learning

Big Data Analytic Paradigms -From PCA to Deep Learning The Intersection of Robust Intelligence and Trust in Autonomous Systems: Papers from the AAAI Spring Symposium Big Data Analytic Paradigms -From PCA to Deep Learning Barnabas K. Tannahill Aerospace Electronics

More information

Web Document Clustering

Web Document Clustering Web Document Clustering Lab Project based on the MDL clustering suite http://www.cs.ccsu.edu/~markov/mdlclustering/ Zdravko Markov Computer Science Department Central Connecticut State University New Britain,

More information

SURVIVABILITY OF COMPLEX SYSTEM SUPPORT VECTOR MACHINE BASED APPROACH

SURVIVABILITY OF COMPLEX SYSTEM SUPPORT VECTOR MACHINE BASED APPROACH 1 SURVIVABILITY OF COMPLEX SYSTEM SUPPORT VECTOR MACHINE BASED APPROACH Y, HONG, N. GAUTAM, S. R. T. KUMARA, A. SURANA, H. GUPTA, S. LEE, V. NARAYANAN, H. THADAKAMALLA The Dept. of Industrial Engineering,

More information

Simulated PV Power Plant Variability: Impact of Utility-imposed Ramp Limitations in Puerto Rico

Simulated PV Power Plant Variability: Impact of Utility-imposed Ramp Limitations in Puerto Rico Simulated PV Power Plant Variability: Impact of Utility-imposed Ramp Limitations in Puerto Rico Matthew Lave 1, Jan Kleissl 2, Abraham Ellis 3, Felipe Mejia 2 1 Sandia National Laboratories, Livermore,

More information

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis Authors Name/s per 1st Affiliation (Author) Authors Name/s per 2nd Affiliation (Author) line 1 (of Affiliation): dept. name

More information

The Weather Intelligence for Renewable Energies Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

The Weather Intelligence for Renewable Energies Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation Energies 2015, 8, 9594-9619; doi:10.3390/en8099594 Article OPEN ACCESS energies ISSN 1996-1073 www.mdpi.com/journal/energies The Weather Intelligence for Renewable Energies Benchmarking Exercise on Short-Term

More information

Scalable Developments for Big Data Analytics in Remote Sensing

Scalable Developments for Big Data Analytics in Remote Sensing Scalable Developments for Big Data Analytics in Remote Sensing Federated Systems and Data Division Research Group High Productivity Data Processing Dr.-Ing. Morris Riedel et al. Research Group Leader,

More information

Support Vector Machines with Clustering for Training with Very Large Datasets

Support Vector Machines with Clustering for Training with Very Large Datasets Support Vector Machines with Clustering for Training with Very Large Datasets Theodoros Evgeniou Technology Management INSEAD Bd de Constance, Fontainebleau 77300, France theodoros.evgeniou@insead.fr Massimiliano

More information

Active Learning SVM for Blogs recommendation

Active Learning SVM for Blogs recommendation Active Learning SVM for Blogs recommendation Xin Guan Computer Science, George Mason University Ⅰ.Introduction In the DH Now website, they try to review a big amount of blogs and articles and find the

More information

A New Quantitative Behavioral Model for Financial Prediction

A New Quantitative Behavioral Model for Financial Prediction 2011 3rd International Conference on Information and Financial Engineering IPEDR vol.12 (2011) (2011) IACSIT Press, Singapore A New Quantitative Behavioral Model for Financial Prediction Thimmaraya Ramesh

More information

Studying Achievement

Studying Achievement Journal of Business and Economics, ISSN 2155-7950, USA November 2014, Volume 5, No. 11, pp. 2052-2056 DOI: 10.15341/jbe(2155-7950)/11.05.2014/009 Academic Star Publishing Company, 2014 http://www.academicstar.us

More information

Spam detection with data mining method:

Spam detection with data mining method: Spam detection with data mining method: Ensemble learning with multiple SVM based classifiers to optimize generalization ability of email spam classification Keywords: ensemble learning, SVM classifier,

More information

Support Vector Machines for Dynamic Biometric Handwriting Classification

Support Vector Machines for Dynamic Biometric Handwriting Classification Support Vector Machines for Dynamic Biometric Handwriting Classification Tobias Scheidat, Marcus Leich, Mark Alexander, and Claus Vielhauer Abstract Biometric user authentication is a recent topic in the

More information

Predict the Popularity of YouTube Videos Using Early View Data

Predict the Popularity of YouTube Videos Using Early View Data 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050

More information

A PUSH FOR GREEN ENERGY

A PUSH FOR GREEN ENERGY TEXT: STEVE ROMAN PHOTO: WÄRTSILÄ [ SOLUTION ] A PUSH FOR GREEN ENERGY Cheap oil or no cheap oil, the move toward clean, renewable energy sources is picking up pace in the US, especially in the windswept

More information

2015 MATLAB Conference Perth 21 st May 2015 Nicholas Brown. Deploying Electricity Load Forecasts on MATLAB Production Server.

2015 MATLAB Conference Perth 21 st May 2015 Nicholas Brown. Deploying Electricity Load Forecasts on MATLAB Production Server. 2015 MATLAB Conference Perth 21 st May 2015 Nicholas Brown Deploying Electricity Load Forecasts on MATLAB Production Server. Executive Summary This presentation will show how Alinta Energy used the MATLAB

More information

Adaptive model for thermal demand forecast in residential buildings

Adaptive model for thermal demand forecast in residential buildings Adaptive model for thermal demand forecast in residential buildings Harb, Hassan 1 ; Schütz, Thomas 2 ; Streblow, Rita 3 ; Müller, Dirk 4 1 RWTH Aachen University, E.ON Energy Research Center, Institute

More information

Support Vector Machines Explained

Support Vector Machines Explained March 1, 2009 Support Vector Machines Explained Tristan Fletcher www.cs.ucl.ac.uk/staff/t.fletcher/ Introduction This document has been written in an attempt to make the Support Vector Machines (SVM),

More information

Solar Input Data for PV Energy Modeling

Solar Input Data for PV Energy Modeling June 2012 Solar Input Data for PV Energy Modeling Marie Schnitzer, Christopher Thuman, Peter Johnson Albany New York, USA Barcelona Spain Bangalore India Company Snapshot Established in 1983; nearly 30

More information

Automated Content Analysis of Discussion Transcripts

Automated Content Analysis of Discussion Transcripts Automated Content Analysis of Discussion Transcripts Vitomir Kovanović v.kovanovic@ed.ac.uk Dragan Gašević dgasevic@acm.org School of Informatics, University of Edinburgh Edinburgh, United Kingdom v.kovanovic@ed.ac.uk

More information

2014 Forecasting Benchmark Survey. Itron, Inc. 12348 High Bluff Drive, Suite 210 San Diego, CA 92130-2650 858-724-2620

2014 Forecasting Benchmark Survey. Itron, Inc. 12348 High Bluff Drive, Suite 210 San Diego, CA 92130-2650 858-724-2620 Itron, Inc. 12348 High Bluff Drive, Suite 210 San Diego, CA 92130-2650 858-724-2620 September 16, 2014 For the third year, Itron surveyed energy forecasters across North America with the goal of obtaining

More information

What the Characteristics of Wind and Solar Electric Power Production Mean for Their Future

What the Characteristics of Wind and Solar Electric Power Production Mean for Their Future What the Characteristics of Wind and Solar Electric Power Production Mean for Their Future Jay Apt Tepper School of Business and Department of Engineering & Public Policy Carnegie Mellon University March

More information

REAL-TIME PRICE FORECAST WITH BIG DATA

REAL-TIME PRICE FORECAST WITH BIG DATA REAL-TIME PRICE FORECAST WITH BIG DATA A STATE SPACE APPROACH Lang Tong (PI), Robert J. Thomas, Yuting Ji, and Jinsub Kim School of Electrical and Computer Engineering, Cornell University Jie Mei, Georgia

More information

A Simple Introduction to Support Vector Machines

A Simple Introduction to Support Vector Machines A Simple Introduction to Support Vector Machines Martin Law Lecture for CSE 802 Department of Computer Science and Engineering Michigan State University Outline A brief history of SVM Large-margin linear

More information

A Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data

A Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data A Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data Athanasius Zakhary, Neamat El Gayar Faculty of Computers and Information Cairo University, Giza, Egypt

More information

Impacts of large-scale solar and wind power production on the balance of the Swedish power system

Impacts of large-scale solar and wind power production on the balance of the Swedish power system Impacts of large-scale solar and wind power production on the balance of the Swedish power system Joakim Widén 1,*, Magnus Åberg 1, Dag Henning 2 1 Department of Engineering Sciences, Uppsala University,

More information

Relational Learning for Football-Related Predictions

Relational Learning for Football-Related Predictions Relational Learning for Football-Related Predictions Jan Van Haaren and Guy Van den Broeck jan.vanhaaren@student.kuleuven.be, guy.vandenbroeck@cs.kuleuven.be Department of Computer Science Katholieke Universiteit

More information

Energy Demand Forecasting Industry Practices and Challenges

Energy Demand Forecasting Industry Practices and Challenges Industry Practices and Challenges Mathieu Sinn (IBM Research) 12 June 2014 ACM e-energy Cambridge, UK 2010 2014 IBM IBM Corporation Corporation Outline Overview: Smarter Energy Research at IBM Industry

More information

For millennia people have known about the sun s energy potential, using it in passive

For millennia people have known about the sun s energy potential, using it in passive Introduction For millennia people have known about the sun s energy potential, using it in passive applications like heating homes and drying laundry. In the last century and a half, however, it was discovered

More information

AUTOMATION OF ENERGY DEMAND FORECASTING. Sanzad Siddique, B.S.

AUTOMATION OF ENERGY DEMAND FORECASTING. Sanzad Siddique, B.S. AUTOMATION OF ENERGY DEMAND FORECASTING by Sanzad Siddique, B.S. A Thesis submitted to the Faculty of the Graduate School, Marquette University, in Partial Fulfillment of the Requirements for the Degree

More information

Methodology For Illinois Electric Customers and Sales Forecasts: 2016-2025

Methodology For Illinois Electric Customers and Sales Forecasts: 2016-2025 Methodology For Illinois Electric Customers and Sales Forecasts: 2016-2025 In December 2014, an electric rate case was finalized in MEC s Illinois service territory. As a result of the implementation of

More information

The Economic Impact of Replacing Coal with Natural Gas for Electricity Production. William A. Knudson. Working Paper 01 0811

The Economic Impact of Replacing Coal with Natural Gas for Electricity Production. William A. Knudson. Working Paper 01 0811 THE STRATEGIC MARKETING INSTITUTE The Economic Impact of Replacing Coal with Natural Gas for Electricity Production William A. Knudson Working Paper 01 0811 80 AGRICULTURE HALL, MICHIGAN STATE UNIVERSITY,

More information

Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence

Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence Artificial Neural Networks and Support Vector Machines CS 486/686: Introduction to Artificial Intelligence 1 Outline What is a Neural Network? - Perceptron learners - Multi-layer networks What is a Support

More information

Drugs store sales forecast using Machine Learning

Drugs store sales forecast using Machine Learning Drugs store sales forecast using Machine Learning Hongyu Xiong (hxiong2), Xi Wu (wuxi), Jingying Yue (jingying) 1 Introduction Nowadays medical-related sales prediction is of great interest; with reliable

More information

Data Mining. Nonlinear Classification

Data Mining. Nonlinear Classification Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15

More information

Solar production prediction based on non linear meteo source adaptation

Solar production prediction based on non linear meteo source adaptation Solar production prediction based on non linear meteo source adaptation Mariam Barque,Luc Dufour, Dominique Genoud, Arnaud Zufferey 1 Bruno Ladevie and Jean-Jacques Bezian 2 1 Institute of Information

More information

Ming-Wei Chang. Machine learning and its applications to natural language processing, information retrieval and data mining.

Ming-Wei Chang. Machine learning and its applications to natural language processing, information retrieval and data mining. Ming-Wei Chang 201 N Goodwin Ave, Department of Computer Science University of Illinois at Urbana-Champaign, Urbana, IL 61801 +1 (917) 345-6125 mchang21@uiuc.edu http://flake.cs.uiuc.edu/~mchang21 Research

More information

A Comparison of Two Techniques for Next-Day Electricity Price Forecasting

A Comparison of Two Techniques for Next-Day Electricity Price Forecasting A Comparison of Two Techniques for Next-Day Electricity Price Forecasting Alicia Troncoso Lora, Jesús Riquelme Santos, José Riquelme Santos, Antonio Gómez Expósito, and José Luís Martínez Ramos Department

More information

Performance Analysis of Data Mining Techniques for Improving the Accuracy of Wind Power Forecast Combination

Performance Analysis of Data Mining Techniques for Improving the Accuracy of Wind Power Forecast Combination Performance Analysis of Data Mining Techniques for Improving the Accuracy of Wind Power Forecast Combination Ceyda Er Koksoy 1, Mehmet Baris Ozkan 1, Dilek Küçük 1 Abdullah Bestil 1, Sena Sonmez 1, Serkan

More information

Artificial Neural Network-based Electricity Price Forecasting for Smart Grid Deployment

Artificial Neural Network-based Electricity Price Forecasting for Smart Grid Deployment Artificial Neural Network-based Electricity Price Forecasting for Smart Grid Deployment Bijay Neupane, Kasun S. Perera, Zeyar Aung, and Wei Lee Woon Masdar Institute of Science and Technology Abu Dhabi,

More information

Industry Environment and Concepts for Forecasting 1

Industry Environment and Concepts for Forecasting 1 Table of Contents Industry Environment and Concepts for Forecasting 1 Forecasting Methods Overview...2 Multilevel Forecasting...3 Demand Forecasting...4 Integrating Information...5 Simplifying the Forecast...6

More information

SVM Ensemble Model for Investment Prediction

SVM Ensemble Model for Investment Prediction 19 SVM Ensemble Model for Investment Prediction Chandra J, Assistant Professor, Department of Computer Science, Christ University, Bangalore Siji T. Mathew, Research Scholar, Christ University, Dept of

More information

Solar and PV forecasting in Canada

Solar and PV forecasting in Canada Solar and PV forecasting in Canada Sophie Pelland, CanmetENERGY IESO Wind Power Standing Committee meeting Toronto, September 23, 2010 Presentation Plan Introduction How are PV forecasts generated? Solar

More information

Knowledge Discovery from patents using KMX Text Analytics

Knowledge Discovery from patents using KMX Text Analytics Knowledge Discovery from patents using KMX Text Analytics Dr. Anton Heijs anton.heijs@treparel.com Treparel Abstract In this white paper we discuss how the KMX technology of Treparel can help searchers

More information

Partnership to Improve Solar Power Forecasting

Partnership to Improve Solar Power Forecasting Partnership to Improve Solar Power Forecasting Venue: EUPVSEC, Paris France Presenter: Dr. Manajit Sengupta Date: October 1 st 2013 NREL is a national laboratory of the U.S. Department of Energy, Office

More information

Value of Distributed Generation

Value of Distributed Generation Value of Distributed Generation Solar PV in Massachusetts April 2015 Overview Distributed energy resources (DERs) like solar photovoltaic (solar PV) systems provide unique value to the electric grid by

More information

Operational experienced of an 8.64 kwp grid-connected PV array

Operational experienced of an 8.64 kwp grid-connected PV array Hungarian Association of Agricultural Informatics European Federation for Information Technology in Agriculture, Food and the Environment Journal of Agricultural Informatics. 2013 Vol. 4, No. 2 Operational

More information

Planning Workforce Management for Bank Operation Centers with Neural Networks

Planning Workforce Management for Bank Operation Centers with Neural Networks Plaing Workforce Management for Bank Operation Centers with Neural Networks SEFIK ILKIN SERENGIL Research and Development Center SoftTech A.S. Tuzla Teknoloji ve Operasyon Merkezi, Tuzla 34947, Istanbul

More information

Machine Learning Algorithms and Predictive Models for Undergraduate Student Retention

Machine Learning Algorithms and Predictive Models for Undergraduate Student Retention , 225 October, 2013, San Francisco, USA Machine Learning Algorithms and Predictive Models for Undergraduate Student Retention Ji-Wu Jia, Member IAENG, Manohar Mareboyana Abstract---In this paper, we have

More information

Research and Development: Advancing Solar Energy in California

Research and Development: Advancing Solar Energy in California Research and Development: Advancing Solar Energy in California Laurie ten Hope Deputy Director Energy Research and Development Division California Energy Commission 2014 UC Solar Research Symposium San

More information

Value of Distributed Generation

Value of Distributed Generation Value of Distributed Generation Solar PV in Rhode Island July 2015 Overview Distributed energy resources (DERs) like solar photovoltaic (solar PV) systems provide unique value to the electric grid by reducing

More information

Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data

Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data CMPE 59H Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Non-linear

More information

Interpreting Solar Power Graphs Demonstration Activity

Interpreting Solar Power Graphs Demonstration Activity Interpreting Graphs Demonstration Activity To the Instructor: SunViewer.net allows students to access daily information from locations across the country using web-based data access. To see an example

More information

Solar Heating Basics. 2007 Page 1. a lot on the shape, colour, and texture of the surrounding

Solar Heating Basics. 2007 Page 1. a lot on the shape, colour, and texture of the surrounding 2007 Page 1 Solar Heating Basics Reflected radiation is solar energy received by collectorsfrom adjacent surfaces of the building or ground. It depends a lot on the shape, colour, and texture of the surrounding

More information

Bankable Data and Interoperability Standards

Bankable Data and Interoperability Standards Alternative Energy E Magazine emagazine Issue Oct / Nov 2012 http://sunspec.org/bankable- data- interoperability- standards/ Bankable Data and Interoperability Standards Data quality derives from the combination

More information

Solar Power Analysis Based On Light Intensity

Solar Power Analysis Based On Light Intensity The International Journal Of Engineering And Science (IJES) ISSN (e): 2319 1813 ISSN (p): 2319 1805 Pages 01-05 2014 Solar Power Analysis Based On Light Intensity 1 Dr. M.Narendra Kumar, 2 Dr. H.S. Saini,

More information

IDENTIFYING BANK FRAUDS USING CRISP-DM AND DECISION TREES

IDENTIFYING BANK FRAUDS USING CRISP-DM AND DECISION TREES IDENTIFYING BANK FRAUDS USING CRISP-DM AND DECISION TREES Bruno Carneiro da Rocha 1,2 and Rafael Timóteo de Sousa Júnior 2 1 Bank of Brazil, Brasília-DF, Brazil brunorocha_33@hotmail.com 2 Network Engineering

More information