Application of ALO-ELM in Load Forecasting Based on Big Data

Abstract

The load of power system changes with the development of economy, short-term load forecasting play a very important role in dispatching and management of power system. In this paper, the Ant Lion Optimizer (ALO) is introduced to improve the input weights and hidden-layer Matrix of extreme learning machine (ELM), after the parameters of ELM are optimized by ALO, then input nodes, hidden layer nodes and output nodes are determined, so a load forecasting model based on ALO-ELM combined algorithm is established. The proposed method is illustrated based on the historical load data of a city in China. The results show that the average absolute error of short-term load demand predicted by ALO-ELM model is 1.41, while that predicted by ELM is 4.34, the proposed ALO-ELM algorithm is superior to the ELM and meet the requirements of engineering accuracy, which proves the effectiveness of proposed method.

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

“…Statistical methods mainly include time series forecasting models (Dong, 2019;Ma et al, 2022;Gao, 2023) smoothing models (Trull et al, 2021). For example, Shang Fangyi et al (Shang et al, 2015) utilized the gray Verhulst model to enhance the precision of electricity demand analysis and forecasting; Zhang Tao et al (Zhang and Gu, 2018) introduced Markov chains into the study of renewable energy load forecasting, and achieved effective results; Luo Yi-wang (Luo, 2018) applied the ARMR model to the study of electricity demand forecasting methods, and claimed that the forecasting errors were less than 1% in all of their studies; Zhang Yunfei et al (Zhang Yunfei et al, 2021) developed a grid peaking demand forecasting model using ridge regression, demonstrating its effectiveness through a case study; Wu et al (He et al, 2021) combined the Seasonal Exponential Adjustment Method (SEAM) with the time series regression method for the study of load demand forecasting and confirmed the superiority of the model. 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, Shang Fangyi et al (Shang et al, 2015) utilized the gray Verhulst model to enhance the precision of electricity demand analysis and forecasting; Zhang Tao et al (Zhang and Gu, 2018) introduced Markov chains into the study of renewable energy load forecasting, and achieved effective results; Luo Yi-wang (Luo, 2018) applied the ARMR model to the study of electricity demand forecasting methods, and claimed that the forecasting errors were less than 1% in all of their studies; Zhang Yunfei et al (Zhang Yunfei et al, 2021) developed a grid peaking demand forecasting model using ridge regression, demonstrating its effectiveness through a case study; Wu et al (He et al, 2021) combined the Seasonal Exponential Adjustment Method (SEAM) with the time series regression method for the study of load demand forecasting and confirmed the superiority of the model. 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.…”