Abstract
Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.
Highlights
Smart grid technologies have attracted much attention because of their potential to cope with climate change and energy crises [1]
We compared it with other state-of-the-art models, such as multivariate random forest (RF) (MRF), DNN, long short-term memory (LSTM), ATT-LSTM, gated recurrent unit (GRU), and ensemble models to evaluate the validity of the proposed model
We implemented an RF-based Short-term load forecasting (STLF) model using MRF [40] in R packages and the two stacking ensemble models using xgboost 1.3.0 [41] and scikit-learn [42] in the Python environment
Summary
Smart grid technologies have attracted much attention because of their potential to cope with climate change and energy crises [1]. Support vector regression (SVR) [7], random forest (RF) [8], and extreme gradient boosting (XGB) [9] have demonstrated excellent prediction performance by considering the nonlinear relationship between input and output variables These STLF models focus on the day-ahead point forecasting; they cannot and properly cope with various unexpected events that could affect electrical energy consumption for a day from the present time. We propose a GRU-based multistep-ahead STLF model and augment it using an attention mechanism to improve the forecasting performance by focusing more on crucial variables. We conducted multistep-ahead forecasting for the hourly power consumption of buildings to adequately cope with sudden changes in power consumption caused by various unexpected events, such as peak and blackout, instead of day-ahead point STLF.
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