Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network

Karthick Kanagarathinam (), S. K. Aruna, S. Ravivarman, Mejdl Safran, Sultan Alfarhood () and Waleed Alrajhi
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Karthick Kanagarathinam: Department of Electrical and Electronics Engineering, GMR Institute of Technology, Rajam 532127, India
S. K. Aruna: Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore 560029, India
S. Ravivarman: Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Shamshabad 501218, India
Mejdl Safran: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia
Sultan Alfarhood: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia
Waleed Alrajhi: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia

Sustainability, 2023, vol. 15, issue 18, 1-18

Abstract: Integrating wind energy forecasting into urban city energy management systems offers significant potential for optimizing energy usage, reducing the carbon footprint, and improving overall energy efficiency. This article focuses on developing a wind power forecasting model using cutting-edge technologies to enhance urban city energy management systems. To effectively manage wind energy availability, a strategy is proposed to curtail energy consumption during periods of low wind energy availability and boost consumption during periods of high wind energy availability. For this purpose, an LSTM-based model is employed to forecast short-term wind power, leveraging a publicly available dataset. The LSTM model is trained with 27,310 instances and 10 wind energy system attributes, which were selected using the Pearson correlation feature selection method to identify crucial features. The evaluation of the LSTM-based forecasting model yields an impressive R 2 score of 0.9107. The model’s performance metrics attest to its high accuracy, explaining a substantial proportion of the variance in the test data. This study not only contributes to advancing wind power forecasting, but also holds promise for sustainable urban energy management, enabling cities to make informed decisions in optimizing energy consumption and promoting a greener, more resilient future.

Keywords: urban city energy management systems; wind energy forecasting; LSTM; short-term wind power forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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