INTERNET SERVICE PROVIDER LOAD FORECAST Peter Dondi, Jakob Germann, Can Iderman ABB Power Automation Ltd. Switzerland Armando Marcuzzi, Alexandre Gal Entreprises Electriques Fribourgeoises EEF, Switzerland SUMMARY One of the main tasks in ensuring the high availability and quality of electrical energy supply is to match the power generation to the load at each instant in time. Since, as opposed to other energy sources, electricity is produced at the time of use, and does not lend itself easily to storage, the matching of supply to demand is of utmost importance to maintain the level of reliable supply that is expected in today s society. Load forecasting tools have long played a role in supporting the utility in fulfilling the task of matching the production to the load requirements. The temporal spectrum of forecasting, ranges from long term planning analysis over many decades used to plan power plant construction, down to short-term forecasts for minutes and hours used in operational generation dispatch. With deregulation, and the change in market structure, the prediction of load and supply requirements has also become important to utilities in order to optimize the scheduling and supply contracts, and thus is of importance to the financial success of the company. In the deregulated market, a good short-term load forecast is of major importance to the hourly, daily and weekly forward planning, and becomes an integral part of the operational and financial business process of the utility. The most common load forecasting tools are standard software packages that are supplied either as stand alone packages or as packages integrated into the control system environment of the utility. However, more recently, the Internet has been used to provide a vastly more efficient and sophisticated way of supplying accurate and reliable load forecasts. Along with secure data collection, and state-of-the-art forecasting methods, an Internet service package can be provided that is an advantage to the user, not only in the accuracy and maintenance of the forecasting model, but also in terms of improved productivity, which such a service provides in comparison to an in-house solution. The paper describes how such a service has been set up, and the experiences gained both by the service provider and a utility that has been using the service for a number of years. The technical aspects of such an Internet service, in comparison to the traditional in-house solution are discussed, along with the contractual arrangements between such a service provider and the utility. The needs of the user of such a service are described, and the results achieved by such a service which has been used in operational conditions. In the conclusion, comments are included on the extensions of such services, and the future of utility data handling and the relationship between functionality and services in the Web-world.
INTERNET SERVICE PROVIDER LOAD FORECAST Peter Dondi, Jakob Germann, Can Iderman ABB Power Automation Ltd. Switzerland Armando Marcuzzi, Alexandre Gal Entreprises Electriques Fribourgeoises EEF, Switzerland Abstract: A load forecasting service that can be accessed via the Internet has been set up and its concept is discussed in this paper. This paper reviews first the load-forecasting problem, then shows the basic functions of the service emphasizing on the technical aspects of its implementation and finally describes field test experiences. The service is currently providing load forecasts for numerous utilities and big industrial companies. Key words: Short Term Load Forecast, Artificial Neural Networks, Internet Services, Application Service Provider, Business Service Provider, Outsourcing Perspectives INTRODUCTION One of the main tasks in ensuring the high availability and quality of electrical energy supply is to match the power generation to the load at each instant in time. Since, as opposed to other energy sources, electricity is produced at the time of use, and does not lend itself easily to storage, the matching of supply to demand is of utmost importance to maintain the level of reliable supply that is expected in today s society. Load forecasting tools have long played a role in supporting the utility in fulfilling the task of matching the production to the load requirements. The temporal spectrum of forecasting, ranges from long term planning analysis over many decades used to plan power plant construction, down to short-term forecasts for minutes and hours used in operational generation dispatch. With deregulation, and the change in market structure, the prediction of load and supply requirements has also become important to utilities in order to optimize the scheduling and supply contracts, and thus is of importance to the financial success of the company. In the deregulated market, a good short-term load forecast is of major importance to the hourly, daily and weekly forward planning, and becomes an integral part of the operational and financial business process of the utility. The most common load forecasting tools are standard software packages that are supplied either as stand alone packages or as packages integrated into the control system environment of the utility. However, more recently, the Internet has been used to provide a vastly more efficient and sophisticated way of supplying accurate and reliable load forecasts. Along with secure data collection, and state-of-the-art forecasting methods, an Internet service package can be provided that is an advantage to the user, not only in the accuracy and maintenance of the forecasting model, but also in terms of improved productivity, which such a service provides in comparison to an in-house solution. The paper describes how such a service has been set up, and the experiences gained both by the service provider and a utility that has been using the service for a number of years. The technical aspects of such an Internet service, in comparison to the traditional in-house solution are discussed, along with the contractual arrangements between such a service provider and the utility. The needs of the user of such a service are described, and the results achieved by such a service which has been used in operational conditions. In the conclusion, comments are included on the extensions of such services, and the future of utility data handling and the relationship between functionality and services in the Web-world. INTERNET LOAD FORECASTING Traditionally, suppliers have provided short-term load forecasting as a software package, which is sold, customized, installed at the customer site and maintained by supplying software upgrades. Typically, there are two chosen methods of installation. The simplest is to provide a stand alone package with manual input of the required data in an off-line situation, without the aid of sophisticated, integrated data checking, standardized MMI and on-line data. However, in other cases, the forecasting package is often supplied as an integrated component of an energy management system (EMS). In this case, the forecasting package is directly linked to the online and historical databases, has the same MMI as the rest of the EMS system, and is directly available to the operational personnel. Although the latter case improves the data handling, there is often reluctance within the utility to rely solely on this application, and in many cases, the former offline package is used. The reasons for this is that in most cases, the EMS system is, for security reasons, relatively isolated from the Utility IT environment, whilst the department handling contracts and forecasting are organizationally separated from operations. Independently of the preferred installation, and especially with the more sophisticated load forecasting methods, the supplier usually takes the responsibility for commissioning the software, initializing the input data, tuning the model and training the utility staff in the use of the tool. However, the usual maintenance contract after the delivery of the package is related to software updates, so that the utility personnel are responsible for the day to day forecasting. The utility must ensure that the personnel using the tool understand the methodology, can handle the forecast modeling, are able to prepare and input the data as required, and in the case of
unusual results, be in a position to interpret the results accordingly. As the importance of the load forecast takes on higher priority in the deregulated market, the utility needs to have a number of staff members available, which can operate the package. Needless to say, this can be both a costly and time-consuming activity, especially when the software is updated, or the algorithm improved, and retraining is required. With the Internet now available as a stable and reliable medium for information exchange, the load forecast has been successfully implemented as an Internet service, which has been running and providing load forecasts for utilities for a number of years. The interaction between user and supplier has changed considerably. The user no longer needs to be involved in the unnecessary tasks of software installation and maintenance, nor the need for specialized training, nor even the daily routine jobs required in preparing the input data and running the load forecast. Thus, the service provided is not just that of an Application Service Provider 1 (ASP) where software packages on the internet are made available for the user, but rather a Business Service Provider (BSP) where a service group fulfils the utility needs to maintain, operate and produce high quality load forecasts. This allows the user to focus on the impacts and opportunities of the deregulated market, instead of trying to keep the load forecasting support infrastructure up-todate, worrying about the problems of maintaining forecasting expertise, and lack of data maintenance resources. SERVICE TECHNOLOGY The effectiveness of the service relies on an integrated package involving system equipment, a successful load forecasting algorithm, a secure data exchange mechanism and a dedicated service team. System architecture The system is composed of: a computation server, where the forecasting procedures are scheduled in order to execute the service task, 1 Providing software on an Application Service Provider (ASP) basis over the Internet is a relatively new concept. The service fee covers software license costs, upgrades and maintenance of the software and hardware, and no installations at the customer site are necessary. Hence, the customer benefits by replacing investment with service fees, and little or no loss of efficiency during upgrades. Furthermore, the web compatibility makes this a very convenient solution for users who have a growing aversion to fragmented non-integrated products. a special firewall, which is able to filter the http protocol and understand the contents of the commands that the user is entering a communication server outside the protected region, which is used as an intermediate data exchange area between the customers and the computing server. Data Description and Exchange The information necessary to make the forecast is composed of several different types of data: historical data of the time series which is provided by the client, calendar data, with notification of holidays, etc., meteorological data, which the service provider organizes from meteorological services. The data can be received automatically, or entered daily via the WWW browser interfaces. At the start of the service, the service provider forecasting specialists develop a mathematical model, fitting the specific needs of every client. Specialized models may be specified for different forecast horizons and for different purposes, such as peak and energy demand forecasts. The forecasting session is composed of the following subtasks: Input/output load and meteorological data to the system Parameterize and run the forecast Define and compose reports Send results to end uses. Graphical User Interfaces The WWW enables the customer to access the remote forecasting system by means of standard graphical interfaces (Figure 1). Data transfer is transparently encrypted to allow secure transmission of load data. The ergonomic interfaces allow fast interaction with the remote-forecasting program. They let the user enter data, run forecasts, visualize and retrieve results and play scenario cases. In automatic mode the user sends load data and receives the results by a number of possibilities, depending on his wishes, either by fax, email, or ftp and the forecasts are run automatically by the system (http://abb.prediction-partner.ch).
the provider in the case that the results are not so accurate. The benefit to the utility is an up-to-date forecast based on a service charge. No investment in training of engineers in forecasting technology is required, and the data maintenance is reduced to providing the service provider with the historical load data. The service provider by operating for more than one utility maintains high productivity and can supply each utility with the expertise that they themselves cannot maintain. Besides the access to increased quality forecasting services, the benefits for the customers among others things are elimination of engineer training costs and elimination of overhead for acquiring and maintaining dedicated software. Load Forecast Attributes Figure 1: Graphical Reports on a WEB page The Load Forecasting Service The forecasting service experts take over the responsibility for monitoring the quality of the load forecasts and update the forecasting models as required. As software updates and extensions are added to the system, they are implemented at the service centre site, and the user is not involved in costly implementation activities in-house. The new features are introduced to the clients who can then take advantage of the extensions as required. The types of load forecast that are available are grouped into a number of products (Figure 2). The contractual service agreements between users and the service centre are organised accordingly. The contracts range from simple monthly payments for the forecast, to the possibility of including an accuracy based contract, which works in favour of the service when the results are in a percentage accuracy range, and against Basic Pack Basic 1 week ahead weekly hourly (½, ¼) forecast Option Pack 1 Plus 1 week ahead, holidays corrected as Saturdays Option pack 2 Daily 1 week ahead daily hourly (½, ¼) forecast Option Pack 3 Holidays 1 week ahead holidays corrections with fine modelling Option Pack 4 High Quality 1 week ahead hourly (½, ¼) forecast including meteorological correction + Quality based fee program Figure 2 Providing a selection of forecasts allows the user to determine his own priorities Independently of the type of load forecasting (long, medium, short term), the availability of measurable or foreseeable information for the model, and the complexity of the computational algorithm, there are general specifications which can be considered good attributes of a load forecasting model, Gross and Galiana[1]: ease of use accuracy of forecasts adaptivity robustness. Ease of use The ease of usage rules can be listed as: automated input and output database operations, automated manual operations and data entering and checking, to free the operator from routine and cumbersome tasks automated input of exogenous variables, direct links with the meteorological service to automate relevant information gathering reports and results simplified as much as possible, and suited to the operational dispatching needs. Typically the ease of use rules are requirements of the type generally expected by any standard package. In particular, an Internet service is fully in line with these basic rules more or less by default. Accuracy of forecasts Accuracy is the most important quality criteria. Regardless of the other benefits of the model (simplicity, ease of implementation, etc.), a forecasting algorithm will be judged mostly from its accuracy. Up to now, this has been obtained a posteriori from the observed error between the forecast value and the realized event. Usually the accuracy is computed as the mean squared error, the mean absolute percentage error and the maximal error in a given period.
The accuracy varies as a function of the horizon, the season of the year and also the hour of the day, reflecting the intrinsic heteroscedasticity of the load shape. The more the observed hourly load has a relatively larger variance, the more difficult it will be to forecast. For instance, evening and night periods are quiet and easier to forecast than hours around load peaks. Reported results indicate mean absolute percentage errors (MAPE) of 0.5% for a one hour, up to 1.5-2.0% for one day ahead and to 2-3% for two days ahead. Adaptivity Adaptivity relates to the ability of a model to adapt its parameters to changes in the physical processes, which otherwise could compromise the forecast accuracy. In the long-term, the electrical load characteristics change due to a structural modification in consumer habits. Demographic changes and economical swings usually cause this In the short term, the accuracy depends more on meteorological variables, whose nonlinear characteristic changes during the year. Robustness The electrical load shape can be perturbed by measurement errors, by unavailable data and by holidays. To implement a good result in the case of such deviations, it is necessary to detect and replace these data in order not to contaminate the database and to allow an unbiased estimation of the model parameters. This is a major requirement, and the implementation of robust estimation techniques should be employed to automatically diminish their effect. Particular attention is paid in providing the user with the requirements set out above, and the latest in algorithmic techniques, Germond et al [2] have been implemented to cope with the quality, adaptivity and robustness, Germond et al [3]. In particular however, the user no longer has a need to know of, what is implemented, how it works, what can go wrong, etc., because he is involved in a service where the expertise lies with the service provider, who is also responsible for the accuracy of the forecast. Advantages for the users No heavy investments for dedicated software, but a monthly subscription, Leading edge technologies and access to the latest research developments, Reduced and more focused work load for the engineers in charge of planning tasks, Improved productivity, Choice of products to tailor to utility needs, Highly stable forecast quality, yielding better energy resource planning, A partnership with a team of specialists which understand and actively participate in the business, A forecasting service accessible 24 hours a day, 7 days a week on the Internet, Standard interfaces and standard communication channels for all input and output. THE PERSPECTIVE OF AN ENERGY COMPANY Les Entreprises Electriques Fribourgeoises (EEF) is a major utility operating in the west of Switzerland, and is among the largest energy distributors in Switzerland. Headquartered in the city of Fribourg, and operating throughout the Canton of Fribourg, as well as in the cantons of Vaud and Bern, EEF distributes electricity to its 135 000 clients spread over the region covering 2 000km 2. The company distributes energy of 1 700 GWh, of which, a little more than 50% are household and commercial. On average, EEF has a production capacity of around 600 GWh. This is generated from its own plants rated at 260 MVA, which are entirely of hydro-electric. The region covered by EEF includes by both rural and urban areas with their typical distribution requirements. As far as load forecasting is concerned, the load levels can be seen to be influenced by cloud coverage, with however the main component being temperature dependence corresponding to wide usage of electrical heating. It should however be reported that during the past few years, a noticeable change in the load pattern has been detected resulting from the installation of air conditioners, which are becoming more and more predominant. The electric load is an essential parameter for optimization of purchase and production of the electrical energy. EEF has been long aware of the importance of load forecasting to support these activities, and the need to develop a load forecast with response to climactic changes as well as to calendar functions such as holidays, vacations etc. Further, the model has to be viable, simple and easy to use and effect immediately current tariffs. To support the needs, EEF has originally implemented the following load forecast system. EEF decided to use historical analysis of the existing measured load data. Days were created as a function of season. Cloud cover was included by making small adjustments to the temperature during a certain portion of the day. The model which has been in use for some time was considered to work satisfactorily in forecasting load behaviour, however the disadvantage was that this model required continuous adaptations and reevaluations to correspond to our distribution. In 1996, ABB was introducing a new auto-regressive neural-network forecasting algorithm, and at the same time a service concept which implied that EEF would no longer have to be involved with the data analysis and modelling requirements. As EEF was already running a
satisfactory load forecast, it was proposed that before agreeing to the new package, a comparison test period be applied during which EEF could draw their own conclusions as to the suitability of the neural-network algorithm. The test period, which was carried out on real load measurements, demonstrated to the satisfaction of EEF the value of the new system both in forecasting quality and simplicity of use. The electricity purchases require a high quality and well performing forecast, and since the test evaluation, EEF has been a subscriber to the ABB load forecasting service. Access to data by the Internet has proven very quick and easy to handle. The requirements of EEF are well served by 7-day ahead forecasts as a basis for purchases, and correction of purchases and production every day as the forecasts are adjusted by the rolling forecasts. With the experience gained in the use of the forecasts and comparison of both forecasts (ABB & EEF), EEF has improved its trading, and although the positive financial impact can not be precisely quantified the improvement is undeniable. With the opening of the markets, new financial products, even though still somewhat foreign to the electricity market (SWAP, Options, Weather Risk, etc), can be used to stabilize risks. EEF is confident that a good load forecasting symbolizes an element of security and represents an assurance to judging purchases and capitalizing on production. OUTLOOK AND CONCLUSION With some years of experience in providing a load forecasting service, and a dedicated specialist team, interaction with the users provides valued input for extensions and improvements, which are then evaluated for implementation. Such interests include: Extension of the Robust Error Correction Meter Reading Corrections Holiday & Vacation Data Corrections Automatic optimization (done beginning of every month) Holidays definable as percent of working day Customer data can be maintained and modified by client Intelligent error reporting, where it happened, what is possible solution, In more and more situations, as market forces take effect, load forecasting as a decision making tool for utilities operating outside of their own area is becoming of major importance. The enormous need for a reliable, accurate forecast increases, especially because the utility personnel can no longer be relied on to judge by experience the new load patterns. At the same time, it is important to be able to automate the exchange of much more market-oriented data, including real-time data such as load profiles, schedules, metering data (EMA data). For this application, direct secure connection making SCADA data of various energy traders accessible to each partner independent of protocols used can now be implemented providing a real-time master database, which can be remotely operated by an Internet interface like a simple browser, Germann and Stagoll [4]. Such advanced applications which bring ASP, and Business Service Providers over Internet in direct communication with online databases form the basis for efficient and secure market conditions, and at the same time ensure high productivity for those participating. The advantages of such solutions, as the load forecasting service discussed in the paper, over a software acquisition demonstrate that such services offer a real alternative in a competitive business environment. Internet based forecasting services can be automated for a large numbers of new clients, deliver excellent results with low investment, and at the same time decreases the unnecessary work load for internal teams at the utility which can then concentrate on core business. The forecasting service is currently providing load forecasts for a number of European utilities. BIBLIOGRAPHY 1 G. Gross and F.D. Galiana, 1987, Short Term Load Forecasting, Proceedings of IEEE, Vol. 75(12), pp. 1558-1573. 2 A. Germond, A. Piras, Y. Jaccard, P. Dondi, K. Imhof, J. Bernasconi, and B. Buchenel, 1996, Lastprognosen mit neuronalen Netzen, SEV Bulletin 21/96, pg.11. 3 A.Germond, A.Piras, Y. Jaccard, B.Buchenel, Heterogeneous Artificial Neural Networks for Short Term Load Forecasting, IEEE Transactions on Power Systems, Vol. 11(1), pp. 397-402 4 J. Germann and Y. Stagoll, 2000, Web based gateway for EMA data for the emerging deregulated markets, Conference Proceedings Distributech 2000, Vienna.