A novel forward operator-based Bayesian recurrent neural network-based short-term net load demand forecasting considering demand-side renewable energy

Abstract

Currently, traditional electricity consumers are now shifting to a new role of prosumers since more integration of renewable energy to demand side. Accurate short-term load demand forecasting is significant to safe, stable, and reliable operation of a renewable energy-dominated power system. In this paper, a short-term load forecasting model based on a bidirectional long short-term memory network (Bi-LSTM) using kernel transfer operator is proposed to achieve short-term load demand forecasting. To consider the influence of seasonality, holiday effects and weather on load demand forecasting, and simultaneously to improve the accuracy and performance of the forecasting model, this paper implements the dimensionality reduction of the input data by introducing an improved kernel transfer operator based on the Perron-Frobenius method. On this basis, a Bayesian Bi-LSTM for short-term load demand forecasting is formulated to obtain the probability prediction interval of load demand. To verify the validity of the proposed method, the actual historical load demand in a certain region in China was used for model training and verification. The forecasting results are compared with several conventional load demand methods using probability prediction technics. Simulation analysis shows that the proposed method can effectively reflect the short-term uncertainty of load demand, and is superior to the conventional methods in terms of forecasting accuracy and computational performance.

Research on renewable energy power demand forecasting method based on IWOA-SA-BILSTM modeling

“…Artificial intelligence forecasting methods include Extreme Learning Machine (ELM) (He et al, 2021), Support Vector Machine (SVM) (Shi et al, 2019;MuSAA et al, 2021), and various neural network forecasting models (Machado et al, 2021;Rajbhandari et al, 2021;Hu et al, 2023). For example, Shi et al (Shi et al, 2012) utilized SVM to forecast the amount of photovoltaic (PV) load generation and claimed that the results were good; Zare-Noghabi et al (Zare-Noghabi et al, 2019) demonstrated the effectiveness of Support Vector Regression (SVR) in forecasting power system load demand using actual data; Guo et al (Guo et al, 2021) developed a load forecasting model using LSTM, considering demand response, and demonstrated its practicality through experiments; Wen et al (Wen et al, 2022) proposed a short-term load demand forecasting model based on Bi-directional Long Short-Term Memory(BILSTM) considering the uncertainty of short-term load demand and claimed that the model was superior to the traditional forecasting methods; Su Chang et al (Su et al, 2023) utilized LSTM and combined it with multi-feature fusion coding to forecast the power load demand, which improved the accuracy of the power load forecasting; Zhang Suning et al (Zhang et al, 2022) proposed a cross-region power demand forecasting model based on XGBoost for different forms of power demand in multiple regions and claimed that the method can provide fast and accurate forecasting of power demand; Shu Zhang et al (Zhang Shu et al, 2021) proposed a neural network forecasting model based on feature analysis of the LSTM, which improves the prediction accuracy of short-term power demand. Hybrid forecasting methods (Qinghe et al, 2022;He et al, 2023;Sekhar and Dahiya, 2023) combine various effective forecasting methods to enhance the accuracy of electricity demand forecasting.…”

Deep learning time pattern attention mechanism-based short-term load forecasting method

“…For example, the literature (Wang et al, 2019a) proposed a deep belief network (DBN)-based model for short-term load forecasting, which is able to learn probability distribution so as to determine future load profiles. Other studies recommended the use of self-recurrent wavelet neural networks (SRWNN) for load forecasting in microgrids by introducing a Levenberg-Marquardt learning algorithm to improve the forecast accuracy for highly volatile and non-smooth time series of microgrid electricity load (Chitsaz et al, 2015), the employment of multi-layer perceptron (MLP) for non-residential building electric load forecasting with analyses of most relevant features (Massana et al, 2015), and the application of recurrent neural networks (RNN) for short-term load forecasting that can effectively handle time-series data (Wen et al, 2022). These models utilize historical data in digital formats to predict future electric load variations.…”