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## Electrical Load Forecasting Using Machine Learning Techniques

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**Electrical Load Forecasting Using Machine Learning**Techniques R. E. Abdel-Aal Center for Applied Physical Sciences (CAPS) Research Institute, KFUPM October 2002**Contents**• Introduction to Load Forecasting: Scope, Need, Applications, Problem, and Techniques • Data-Based Modeling Approach: - Neural Networks: Limitations - Abductive Networks: Advantages • Proposed Work • Relevant CAPS Experience • Conclusions**Load Forecasting:**Scope • Long-Term (5-20 Years) • Medium Term (1 month-5 Years) • Short-Term (STLF) (1 hour-1 Week) • Daily Peak Load • Total Daily Energy • Hourly Day Load Curve • Very Short-Term, Real-Time (RTLF) (Mins-Hrs)**Accurate Short-Term Load Forecasting:**Key to Efficient and Secure Operation • Load Over-estimated: - Reserve units spinned-up unnecessarily • Load Under-estimated: - Expensive peaking units - Costly emergency power purchases • Deregulation, competition, and higher loads: - Greater efficiency, lower % forecasting errors - 1% error costed £10 M annually in 1985 - Need faster, more accurate, and more frequent forecasts**Short-Term Load Forecasting:**Applications • Scheduling Functions: Unit commitment, Hydro-thermal coordination, Short-term maintenance, Fuel allocation, Power interchange and transaction evaluation… • Network Analysis Functions: Dispatcher power flow, Optimal power flow • Security and Load Flow Studies: Contingency planning, Load shedding, Security strategies**Short-Term Load Forecasting Problem**Hourly load over a week Daily peak load over a year Summer-Peaking Utility**Short-Term Load Forecasting Problem:**Factors affecting the load • Economic, Environmental: (Slow) Population, industrial growth, electricity pricing, … • Time, Calendar: Daily, weekly, seasonal, holidays, school year, … • Weather: (Heating/cooling loads) Temperature, humidity, wind speed, cloud cover, … • Random Events: Start/stop of large loads, Sports and TV events, …**Daily Load Curve Forecasting Problem:**• Estimate future load Le(d,h) from knowledge of day type, previous hourly loads, previous temperatures, forecasted temperatures, etc… Le(d,h) = F [L(d-1,h), L(d-1,h-1), …, L(d-7,h), L(d-7,h-1), …, T(d-1,h), Te(d,h), … ] • Need to determine optimum inputs and the model relationship**Short-Term Load Forecasting Methods:**Conventional Techniques • Human experts, e.g. using ‘Similar day method’: Slow, unreliable, few experts available. • Statistical univariate time series analysis: ARMA, ARIMA (Box-Jenkins), Kalman Filtering Ignores important weather factors, computationally intensive, user intervention. • Statistical multivariate ‘causal’ regression analysis: Usually linear, difficult to determine correct model relationship, impose own conceptions.**Short-Term Load Forecasting Methods:**Artificial Intelligence/Machine Learning Approaches • Knowledge-Based: e.g. Expert Systems - Accurate knowledge is not always available - Difficult to extract from human experts and encode into computers • Data-Based: e.g. Neural Networks - Little or no a priori knowledge of modeled phenomena is necessary - Utilize abundantly available historical data available at utilities**Short-Term Load Forecasting:**Data-based Computational Intelligence Methods • Can model complex and nonlinear load functions directly from data. • Soft computing- More tolerant to noise, uncertainty, and missing data. • Faster to develop, easier to update. • Heavy computations required only once, during model synthesis.**Data-based Modeling: Supervised Learning Procedure**• Database of solved examples (input-output records) • Split into training and evaluation datasets • Training, with neural networks: - Start with random weights for the network - Apply training inputs, calculate outputs, and compare with known outputs - Adjust weights, and iterate to minimize total output error • Evaluation: - See how model performs on the evaluation set • Actual use: - Apply successful model to practical setting**S**S S The Neural Network (NN) ApproachExample of a day peak forecaster Output: Tomorrow’s Peak Load Inputs: Today’s Peak Load .6 0.5 .4 .2 0.6 .5 .1 Today’s Tmax 43 .2 .3 .8 .7 48 .2 Tomorrow’s Forecasted Tmax Dependent variable Prediction Independent variables Weights Hidden Layer Weights**Limitations of the NN Technique**• Ad hoc approach for determining the fixed network structure and the training parameters • Opacity and black-box nature lead to poor explanation capabilities • Significant input variables are not immediately obvious from model • When to stop training to avoid over-learning? • Local Minima may prevent reaching optimum solution**Self-Organizing Abductive Networks**“Double” Element: y = w0 + w1 x1 + w2 x2 + w3 x12 + w4 x22 + w5 x1 x2 + w6 x13 + w7 x23 - Network of polynomial functional elements- not simple neurons - No fixed a priori model structure. Evolves with training - Network size, element types, connectivity, inputs used, and coefficients are all determined automatically - Automatic stopping criteria, with simple control on complexity - Analytical input-output relationships**Advantages of Abductive Networks**• More automated model synthesis • Automatic selection of effective inputs • Automatic stopping criteria giving good generalization • Faster model development • Reduced user intervention • Simple control on model complexity • Analytical expressions. Better explanation facilities. Easier comparison with regression/empirical models. • Models are easier to export to other applications**Abductive Networks at CAPS**• Modeling/forecasting electric energy consumption • Modeling/forecasting meteorological data • Modeling of petrochemical processes • Oil and gas reservoir characterization • Medical diagnostics • Identification/Determination of radioisotopes and peak fitting in nuclear spectroscopy • Online monitoring of vibrations on vacuum pumps. • Direct estimation of noisy sinusoids**Proposed Work**Apply abductive networks data-based modeling to the important areas of: • Electrical load modeling and forecasting at power utilities of the kingdom. • Hourly air temperature forecasts that may be required.**Benefits to Client**• Transparent and accurate forecasters for economic and reliable operation • Comparison with existing models • Improve understanding of daily, weakly, and seasonal load variations • Determine social, economic, and weather factors influencing load • Introduce the use of modern computational intelligence techniques • Train junior engineers in load forecasting**Outline of Work**1. Identify application area 2. Determine relevant input variables 3. Select data sets for model development 4. Data preprocessing: Scan for outliers and missing data, trend adjustment, normalization, transformations, … 5. Model development 6. Model evaluation and analysis 7. Model integration into client setup 8. Assess performance, compare with present practices.**Examples of relevant modeling and forecasting applications**at CAPS • Monthly electrical energy consumption in the Eastern Province • Daily maximum temperatures at Dhahran • Hourly electrical load forecasting using data from the USA**Modeling the Monthly Electrical Energy Consumption in the**Eastern Province • Domestic Electrical Energy Consumption was modeled in terms of six exogenous parameters • 6-year data: (5 years for training, 1 year forecasted for evaluation) • Derived analytical model relationships from simplified models**Monthly Electrical Energy Consumption:The data set**• Six Inputs: • Month Index (m): m=1,2,…,72 • Monthly average of the global solar radiation (S) • Population (P) • Gross domestic product per capita (G) • Monthly average of the daily mean air temperature (T) • Monthly average of the daily mean relative humidity (H) • One Output: • Monthly Domestic Electrical Energy Consumption(E)**S**P G Monthly Electrical Energy Consumption: The Model Automatically selects the most relevant inputs as: m, H, and T Ignores remaining inputs Gives an overall analytical model relationship**Training**Evaluation Aug 1987 Monthly Electrical Energy Consumption:Model Performance MAPE Error over Evaluation year: 5.6% Previous regression model gave MAPE = 9.2%.**Modeling the Maximum Daily**Air Temperature (TX) • TX was modeled in terms of average temperatures (TA) for the previous three days: TX (d+1) = F [TA (d-2), TA (d-1), TA (d)] • 1987 year data for training, 1988 data for evaluation. • Derived analytical model relationships.**Maximum Daily Air Temperature (TX): The Model**TX(d+1) = 5.243 + 0.272 TA (d-2) – 0.589 TA (d-1) + 1.339 TA (d)**Maximum Daily Air Temperature (TX):Model Performance**Evaluation on 1988 data: MAE = 2.1 °C**Hourly electrical load forecasting Using Abductive Networks**• Hourly load and temperature data for 6 years (1985-1990) from Puget Power, Seattle, USA* • 5-year data (1985-89) for model training and 1990 data for evaluation. • Developed 24 dedicated models that forecast tomorrow’s hourly load curve for any day of the year. ______________________________________________ * Courtesy Professor M. A. El-Sharkawi, University of Washington, Seattle, USA.**The Data Set**Available Data: • 24 daily hourly loads (L1,L2,…,L24), MW • 24 daily hourly temperatures (T1,T2,…,T24), °F Generated Data: • Tmax and Tmin from hourly temperatures • Used actual Tmax and Tmin for next day as forecasted valuesETmax and ETmin. • Classified the forecasted day as: Working day, Saturday, Sunday, or Holiday. Represented as 4 binary inputs.**Average Annual Upward Trend: 3.6%**Evaluation:364 Records 1985 Training: 1821 Records 1989 1990 Trend Removed by normalizing to 1989 mean**2**Hourly Load Forecasters 24 Load Forecaster for Hour h 24 off L(i), Hourly Loads on day (d-1) Load Forecaster for Hour h 2 Tmin, Tmax on day (d-1) 1 Tmine, Tmaxe Estimated for day d Le(d,h) Forecasted Load at hour h, day d 4 Day type code for day d Total : 32 inputs**Examples of Hourly Load Forecasters:Hour 1 (Midnight) Model**Structure: Out of the 32 inputs, only 3 load inputs are selected No temperature inputs No day-type inputs 1-layer nonlinear model X1 = -4.52 + 0.00303 L3 X2 = -4.66 + 0.00295 L20 X3 = -5.61 + 0.00315 L24 Y = 0.125 X1 + 0.868 X3 – 0.115 X1 X2 + 0.0506 X1 X3 + 0.0582 X2 X3 LE1 = 1600 + 312 Y**Hour 1 (Midnight)**Model Performance:**Examples of Hourly Load Forecasters:Hour 12 (Midday) Model**Structure: More complex, 4-layers Only 4 load inputs, including same hour on previous day Only Sunday day-type input Forecasted temperature inputs**Hour 12 (Midday)**Model Performance:**Forecasting Error Statistics Over the 1990**Evaluation Year: Overall MAPE = 2.67 %, with the following distribution: Overall MPE = - 0.16 %, mainly due to error in estimating growth for the forecasting year**Conclusions**• Apply abductive networks machine learning to load modeling and forecasting. • Many advantages over neural networks, e.g. faster modeling and better explanations. • CAPS have used the technique in many areas, including energy, load, and meteorological forecasting. • Benefits include greater forecasting accuracy (reduced operating cost, improved security) and better insight into the load function.