Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System

Yongjie Yang, Yulong Li, Yan Cai, Hui Tang and Peng Xu ()
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Yongjie Yang: School of Information Science and Technology, Nantong University, Nantong 226019, China
Yulong Li: School of Information Science and Technology, Nantong University, Nantong 226019, China
Yan Cai: School of Information Science and Technology, Nantong University, Nantong 226019, China
Hui Tang: School of Information Science and Technology, Nantong University, Nantong 226019, China
Peng Xu: School of Information Science and Technology, Nantong University, Nantong 226019, China

Energies, 2024, vol. 17, issue 15, 1-20

Abstract: In order to address the issues of significant energy and resource waste, low-energy management efficiency, and high building-maintenance costs in hot-summer and cold-winter regions of China, a research project was conducted on an office building located in Nantong. In this study, a data-driven golden jackal optimization (GJO)-based Long Short-Term Memory (LSTM) short-term energy-consumption prediction and optimization system is proposed. The system creates an equivalent model of the office building and employs the genetic algorithm tool Wallacei to automatically optimize and control the building’s air conditioning system, thereby achieving the objective of reducing energy consumption. To validate the authenticity of the optimization scheme, unoptimized building energy consumption was predicted using a data-driven short-term energy consumption-prediction model. The actual comparison data confirmed that the reduction in energy consumption resulted from implementing the air conditioning-optimization scheme rather than external factors. The optimized building can achieve an hourly energy saving rate of 6% to 9%, with an average daily energy-saving rate reaching 8%. The entire system, therefore, enables decision-makers to swiftly assess and validate the efficacy of energy consumption-optimization programs, thereby furnishing a scientific foundation for energy management and optimization in real-world buildings.

Keywords: short-term energy-consumption forecast; modeling and simulation; energy consumption optimization; energy consumption monitoring; energy saving and consumption reduction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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