Fusion of Hierarchical Optimization Models for Accurate Power Load Prediction

Sicheng Wan, Yibo Wang, Youshuang Zhang, Beibei Zhu, Huakun Huang () and Jia Liu ()
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Sicheng Wan: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Yibo Wang: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Youshuang Zhang: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Beibei Zhu: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Huakun Huang: School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Jia Liu: School of Civil Engineering, Guangzhou University, Guangzhou 510006, China

Sustainability, 2024, vol. 16, issue 16, 1-23

Abstract: Accurate power load forecasting is critical to achieving the sustainability of energy management systems. However, conventional prediction methods suffer from low precision and stability because of crude modules for predicting short-term and medium-term loads. To solve such a problem, a Combined Modeling Power Load-Forecasting (CMPLF) method is proposed in this work. The CMPLF comprises two modules to deal with short-term and medium-term load forecasting, respectively. Each module consists of four essential parts including initial forecasting, decomposition and denoising, nonlinear optimization, and evaluation. Especially, to break through bottlenecks in hierarchical model optimization, we effectively fuse the Nonlinear Autoregressive model with Exogenous Inputs (NARX) and Long-Short Term Memory (LSTM) networks into the Autoregressive Integrated Moving Average (ARIMA) model. The experiment results based on real-world datasets from Queensland and China mainland show that our CMPLF has significant performance superiority compared with the state-of-the-art (SOTA) methods. CMPLF achieves a goodness-of-fit value of 97.174% in short-term load prediction and 97.162% in medium-term prediction. Our approach will be of great significance in promoting the sustainable development of smart cities.

Keywords: hierarchical optimization models; deep learning; power load forecasting; ARIMA; NARX; LSTM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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