Development and Comparison of Two Computational Intelligence Algorithms for Electrical Load Forecasts with Multiple Time Scales

Electricity load prediction is an essential tool for power system planning, operation and management. The critical information it provides can be used by energy providers to maximise power system operation efficiency and minimise system operation costs. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are two suitable methods that have been successfully used for analysing time series problems. In this paper, the two algorithms are explored further for load prediction; two load prediction algorithms are developed and verified by using the half-hourly load data from the University of Warwick campus energy centre with four different prediction time horizons. The novelty lies in comparing and analysing the prediction accuracy of two intelligent algorithms with multiple time scales and in exploring better scenarios for their prediction applications. High-resolution load forecasting over a long range of time is also conducted in this paper. The MAPE values for the LSTM are 2.501%, 3.577%, 25.073% and 69.947% for four prediction time horizons delineated. For the SVM, the MAPE values are 2.531%, 5.039%, 7.819% and 10.841%, respectively. It is found that both methods are suitable for shorter time horizon predictions. The results show that LSTM is more capable of ultra-short and short-term forecasting, while SVM has a higher prediction accuracy in medium-term and long-term forecasts. Further investigation is performed via blind tests and the test results are consistent.

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Development and Comparison of Two Computational Intelligence Algorithms for Electrical Load Forecasts with Multiple Time Scales

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Cited by 2 publications

References 21 publications

Demand-Side Management Strategy for Local Energy System Supporting Renewable Energy Sources Integration

Demand-Side Management Strategy for Local Energy System Supporting Renewable Energy Sources Integration

High-Resolution Load Forecasting on Multiple Time Scales Using Long Short-Term Memory and Support Vector Machine

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