The Role of Predictive Models in Energy Efficiency Optimization of Industrial Plants and Buildings

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1 The Role of Predictive Models in Energy Efficiency Optimization of Industrial Plants and Buildings Petr Stluka, Jiri Rojicek, Karel Marik Honeywell Automation and Control Solutions 25 March 2009, Prague

2 Outline Energy Efficiency Applications - Industrial and building segments - Energy supply side / demand side Decision Making Scenarios - Process industries: refining - Cogeneration and buildings Energy Demand Forecasting Methods - Memory-Based Regression 2 Towards e-environment, Mar , Prague

3 Energy Efficiency Terminology - Energy efficiency = using less energy to provide the same level of service e.g. by using more energy efficient appliances - Energy conservation = using less energy to achieve a lesser energy service e.g. through behavioral change Buildings - Energy efficient appliances (refrigerators, freezers, ovens, washers, ) - Monitoring and control of major energy loads (heating, ventilation, air conditioning, lighting, ) Industry - Optimized energy conversion fuel switching, running equipment at its peak efficiency - Advanced boilers and furnaces (combustion) - Selection of less energy intensive operating modes 3 Towards e-environment, Mar , Prague

4 Energy Efficiency Applications Advanced Control Industrial Utilities Industrial Process Plants Public Utilities Steam, power Renewable Energy Dynamic Energy Targeting and Monitoring Smart-grid solutions Grid Homes & Buildings Local Poly-generation (CHP) Optimal Equipment Scheduling Electricity, heating, cooling Energy Monitoring HVAC Control Demand-Response Capable Homes 4 Towards e-environment, Mar , Prague

5 Energy Efficiency Optimization Utility Plants CHP Process Plants Manufacturing Buildings Homes Business objective Generate utilities (power, steam, heating, cooling) to meet customer demands (contracts) Produce mix of products to meet demands in the downstream industries Depends on the type of building (office, hospital, university, army base, shopping center, store, home, etc.) Attitude to energy Energy directly is the primary business objective Second largest cost (after cost of raw materials) Differs by the type of activity. Sometimes energy cost is only a fraction of other operating costs Energy efficiency Consistently addressed in the plants Minimize energy use but delivery of products is the first priority Minimize energy use but keeping occupants comfort is the first priority Approach Closed-loop control Hierarchical optimization Dynamic targeting Continuous improvement Energy monitoring Continuous improvement Energy Demand Dominated by production schedule Dominated by occupants needs 5 Towards e-environment, Mar , Prague

6 Oil Refinery Operation is mostly determined by the specific crude oil and its properties (e.g. sulfur content) Crude oil Crude Oil Distillation Straight Run Gasoline Kerosene Naphtha Hydrogen Gas oil Heavy gas oil Gas Catalytic Reforming Alternate Hydro- Cracking Minimizing energy use and environmental impact by selecting less energy intensive modes on each process unit Gas Straight Run Gasoline High octane Gasoline Gas Recovery and Gasoline Stabilization High octane gasoline and gas Furnace oil or Catalytic Cracking diesel oil Liquefied petroleum gas Isobutylene Butylene Butane Isomerization Isobutane Stabilized gasoline Kerosene Light furnace oil Furnace oil Diesel oil Alkylation Alkylate Di-isobutylene Polymerization Hydrogenation Iso-octane Blending Mix of products to be produced to meet orders Motor & aviation gasoline Cooking Airplanes Automobiles Farming Home heating Jet Fuel Railroads Trucks Asphaltic residue Lubricating residue Residue Atmospheric or Vacuum Distillation on Vacuum Distillation Vacuum Distillation Heavy fuel oils Alternate Hydrotreating Asphalts Heavy gas oil Lubricating distillates Lubricating residue Alternate Coking Solvent Refining Solvent Refining Heavy fuel oils Heavy fuel oils Dewaxing Decolorizing Light lubricating oils Heavy lubricating oils Wax Light products to recovery and stabilization Blending and Packaging Wax Refining Plant Refined Waxes Factories 6 Towards e-environment, Mar , Prague Asphalts Asphalts Ships Sale Roads Waxes Coke

7 Plant-Wide Optimization Criterion Plant profit can be formulated as: Plant Profit = ( Product ) Product rate value ( Raw Raw ) material material usage * * Maintenance costs cost ( Utility Utility ) consumption value * Utility consumption can be estimated potentially for each unit based on the previously built model that relates energy consumption with capacity utilization, operating modes, etc. - Penalties for GHG emissions Closely related to on-site energy generation, types of fuels used, other costs overall energy efficiency, etc. - 7 Towards e-environment, Mar , Prague

8 Predictive Model for Crude Distillation Unit Correlates energy consumption on CDU unit with production targets that uniquely define the mode of operation Predicted energy consumption Fuel energy (preheat, tower, total) Steam energy (tower, side strippers, total) Power energy (tower, pump-arounds, total) Feed Feed flow rate Feed temperature API density Assay/blend properties Disturbances Weather CDU Products Saturated gas flow rate Naphtha flow rate Kerosene flow rate Diesel flow rate AGO flow rate Residue flow rate Naphtha distillation D86 90% Rec Kerosene distillation D86 10% Rec Diesel distillation D86 90% Rec Diesel sulfur wt% 8 Towards e-environment, Mar , Prague

9 CHP and Buildings Energy Suppliers electricity gas Energy Conversion CCHP electricity heat cooling steam Energy User Real time pricing Variable efficiency Fluctuating demand Conversion efficiency of the CHP system is optimized - Gas turbines, steam turbines, steam boilers, hot water boilers, compressor chillers, absorption chillers Energy demand primarily depends on - People s behavior How many occupants are in the building? What they are doing? - Weather Do we need cooling or heating? How much is needed to keep the adequate comfort level? 9 Towards e-environment, Mar , Prague

10 Various Daily Profiles Daily Consumption Profiles commerce two-shift manufacturing casino administration 10 Towards e-environment, Mar , Prague

11 Structure of the Predictive Model Data age Time of day Holiday Ambient temperature Wind velocity Humidity Inputs Demand Model Predictions Heating demand Cooling demand Electricity demand Steam demand Accuracy of energy demand predictions depends heavily on accuracy of weather forecasts 11 Towards e-environment, Mar , Prague

12 Energy Demand Modeling Techniques Heuristics and benchmarks - Rule of thumb, benchmarking of similar units First principle models - Based on thermodynamics, mass and energy balances Time series models - ARMA with exogenous variables (e.g. outdoor temperature) Statistical regression - Basic regression functions are determined based on the knowledge how the manufacturing plant is operated - Resulting global models need to be regularly updated - Linear regression, Partial Least Squares (PLS) Local regression - Regression models are built based on understanding of variations and correlations in historical data - Local modeling deals well with non-linear dependencies, and with segmentation of data in clusters - Also known as: memory-based regression, locally weighted regression (non-parametric statistics) 12 Towards e-environment, Mar , Prague

13 Local Modeling Local regression models are built on-the-fly Y Y X 1 X 2 X 2 Y X 2 X 1 X 1 Current state and its neighborhood (= past operating points similar to the current one) Y X 1 X 2 the dependency Y=f(X 1,X 2 ) is much simpler in the local neighborhood than in global context 13 Towards e-environment, Mar , Prague

14 Local Regression Points in the neighborhood are weighted according to Kernel function w = exp -3σ d 2 2 Forecast y (load) Bandwidth Polynomial fit Distance function d 2 = N i=1 X i* -X i h i 2 Local neighborhood Query point x 0 Independent variable x (time of day) 14 Towards e-environment, Mar , Prague

15 Forecasting Results Forecasted load Forecasted load maximum Actual load Forecasted load minimum Past data Forecasts three days ahead 15 Towards e-environment, Mar , Prague

16 Thank you for your attention!