An Improved Neural Network Algorithm for Energy Consumption Forecasting

Jing Bai, Jiahui Wang, Jin Ran, Xingyuan Li () and Chuang Tu
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Jing Bai: Economic and Management College, Yanshan University, Qinhuangdao 066004, China
Jiahui Wang: Economic and Management College, Yanshan University, Qinhuangdao 066004, China
Jin Ran: Xinjiang Key Laboratory of Green Construction and Smart Traffic Control of Transportation Infrastructure, Xinjiang University, Urumqi 830017, China
Xingyuan Li: Xinjiang Key Laboratory of Green Construction and Smart Traffic Control of Transportation Infrastructure, Xinjiang University, Urumqi 830017, China
Chuang Tu: Economic and Management College, Yanshan University, Qinhuangdao 066004, China

Sustainability, 2024, vol. 16, issue 21, 1-19

Abstract: Accurate and efficient forecasting of energy consumption is a crucial prerequisite for effective energy planning and policymaking. The BP neural network has been widely used in forecasting, machine learning, and various other fields due to its nonlinear fitting ability. In order to improve the prediction accuracy of the BP neural network, this paper introduces the concept of forecast lead time and establishes a mathematical model accordingly. Prior to training the neural network, the input layer data are preprocessed based on the forecast lead time model. The training and forecasting results of the BP neural network when and when not considering forecast lead time are compared and verified. The findings demonstrate that the forecast lead time model can significantly improve the prediction speed and accuracy, proving to be highly applicable for short-term energy consumption forecasting.

Keywords: neural network; energy consumption forecasting; forecast lead time; short-term forecasting (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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