Abstract
Accurate electrical load forecasting plays an important role in power system operation. An effective load forecasting approach can improve the operation efficiency of a power system. This paper proposes the seasonal and trend adjustment attention encoder–decoder (STA–AED), a hybrid short-term load forecasting approach based on a multi-head attention encoder–decoder module with seasonal and trend adjustment. A seasonal and trend decomposing technique is used to preprocess the original electrical load data. Each decomposed datum is regressed to predict the future electric load value by utilizing the encoder–decoder network with the multi-head attention mechanism. With the multi-head attention mechanism, STA–AED can interpret the prediction results more effectively. A large number of experiments and extensive comparisons have been carried out with a load forecasting dataset from the United States. The proposed hybrid STA–AED model is superior to the other five counterpart models such as random forest, gradient boosting decision tree (GBDT), gated recurrent units (GRUs), Encoder–Decoder, and Encoder–Decoder with multi-head attention. The proposed hybrid model shows the best prediction accuracy in 14 out of 15 zones in terms of both root mean square error (RMSE) and mean absolute percentage error (MAPE).
Highlights
Power load forecasting is to study the law of power development and build models between power demand and characteristics based on historical data, and forecast future load [1]
The various approaches developed for Short-term load forecasting (STLF) can be divided into three categories: (1) Traditional statistical methods, for instance, ARMAX [3], ARIMA [4], and autoregressive based time varying model [5]; (2) the artificial intelligence (AI) methods, such as support vector regression (SVR) [6], artificial neural networks (ANN) [7], and gradient boosting [8]; and (3) the hybrid method, such as hybridizing extended Kalman Filter (EKF) and ELM [9], a hybrid STLF approach integrating linear regression and neural network [10], and wavelet neural network [11]
We developed a hybrid approach for short-term load forecasting, which combines the seasonal and trend adjustment technique and multi-head attention-based encoder–decoder framework
Summary
Power load forecasting is to study the law of power development and build models between power demand and characteristics based on historical data, and forecast future load [1]. By using an LSTM-based method to exploit the long-term dependencies of electric load time series, the prediction accuracy of load forecasting is improved [17]. An approach for short-term load forecasting was developed by integrating a regression model with a seasonal exponential adjustment method [26]. To gain better load forecasting results, it is necessary to consider seasonality differences and trend of the data To this end, we developed a hybrid approach for short-term load forecasting, which combines the seasonal and trend adjustment technique and multi-head attention-based encoder–decoder framework. We developed a hybrid approach for short-term load forecasting, which combines the seasonal and trend adjustment technique and multi-head attention-based encoder–decoder framework We named this framework seasonal and trend adjustment attention encoder–decoder (STA–AED).
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