Improving the efficiency and sustainability of intelligent electricity inspection: IMFO-ELM algorithm for load forecasting
College
Ramon V. Del Rosario College of Business
Document Type
Article
Source Title
Sustainability
Volume
14
Publication Date
2022
Abstract
Electricity inspection is important to support sustainable development and is core to the marketing of electric power. In addition, it contributes to the effective management of power companies and to their financial performance. Continuous improvement in the penetration rate of new energy generation can improve environmental standards and promote sustainable development, but it creates challenges for electricity inspection. Traditional electricity inspection methods are time-consuming and quite inefficient, which hinders the sustainable development of power firms. In this paper, a load-forecasting model based on an improved moth-flame algorithm-optimized extreme learning machine (IMFO-ELM) is proposed for use in electricity inspection. A chaotic map and improved linear decreasing weight are introduced to improve the convergence ability of the traditional moth-flame algorithm to obtain optimal parameters for the ELM. Abnormal data points are screened out to determine the causes of abnormal occurrences by analyzing the model prediction results and the user’s actual power consumption. The results show that, compared with existing PSO-ELM and MFO-ELM models, the root mean square error of the proposed model is reduced by at least 1.92% under the same conditions, which supports the application of the IMFO-ELM model in electricity inspection. The proposed power-load-forecasting-based abnormal data detection method can improve the efficiency of electricity inspection, enhance user experience, contribute to the intelligence level of power firms, and promote their sustainable development.
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Recommended Citation
Tian, X., Zuou, Y., Tseng, M., Li, H., & Zhang, H. (2022). Improving the efficiency and sustainability of intelligent electricity inspection: IMFO-ELM algorithm for load forecasting. Sustainability, 14 Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/14371
Disciplines
Technology and Innovation
Keywords
Electric power—Inspection; Sustainable development
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