[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120447-en":3,"doc-seo-120447-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120447,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Instantaneous Electricity Peak Load Forecasting Using Optimization and Machine Learning","Accurate instantaneous electricity peak load prediction is essential for efficient capacity planning and cost-effective grid development. The study improves prediction accuracy by integrating optimization and machine learning models, analyzing how different independent input combinations affect peak load estimates via multilinear regression. Using input data from 1980–2020, the model forecasts peak load with evaluation by MAE, MSE, MAPE, RMSE, and R2, and compares optimization methods including PSO, DO, and GRO against SVR and ANN. Results show ANN and GRO achieve the lowest errors and a strong positive relationship between GDP and peak load.","energies   \nArticle  \nInstantaneous Electricity Peak Load Forecasting Using Optimization and Machine Learning  \nMustafa Saglam 1, *, Xiaojing Lv 2, Catalina Spataru 1 and Omer Ali Karaman 3  \nCitation: Saglam, M.; Lv, X.; Spataru, C.; Karaman, O.A. Instantaneous Electricity Peak Load Forecasting Using Optimization and Machine Learning. Energies 2024, 17, 777 . [https://doi.org/10.3390/en17040777](https://doi.org/10.3390/en17040777)  \nAcademic Editors: Jianjian Shen, Muhammad Sulaiman, Abdellah Salhi and Ashfaq Ahmad  \nReceived: 30 December 2023  \nRevised: 23 January 2024  \nAccepted: 2 February 2024  \nPublished: 6 February 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Energy Institute, Bartlett School Environment, Energy and Resources, University College London, London WC1E 6BT, UK; [c.spataru@ucl.ac.uk](c.spataru@ucl.ac.uk)  \n2 China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 201306, China; [lvxiaojing@sjtu.edu.cn](lvxiaojing@sjtu.edu.cn)  \n3 Department of Electronic and Automation, Vocational School, Batman University, Batman 72100, Türkiye; [omerali.karaman@batman.edu.tr](omerali.karaman@batman.edu.tr)  \n* [Correspondence: mustafa.saglam@ucl.ac.uk](Correspondence: mustafa.saglam@ucl.ac.uk)  \nAbstract: Accurate instantaneous electricity peak load prediction is crucial for efficient capacity planning and cost-effective electricity network establishment. This paper aims to enhance the accuracy of instantaneous peak load forecasting by employing models incorporating various optimization and machine learning (ML) methods. This study examines the impact of independent inputs on peak load estimation through various combinations and subsets using multilinear regression (MLR) equations. This research utilizes input data from 1980 to 2020, including import and export data, population, and gross domestic product (GDP), to forecast the instantaneous electricity peak load as the output value. The effectiveness of these techniques is evaluated based on error metrics, including mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and R2. The comparison extends to popular optimization methods, such as particleswarm optimization (PSO), and the newest method in the field, including dandelion optimizer (DO) and gold rush optimizer (GRO). This comparison is made against conventional machine learning methods, such as support vector regression (SVR) and artificial neural network (ANN), in terms of their prediction accuracy. The findings indicate that the ANN and GRO approaches produce the least statistical errors. Furthermore, the correlation matrix indicates a robust positive linear correlation between GDP and instantaneous peak load. The proposed model demonstrates strong predictive capabilities for estimating peak load, with ANN and GRO performing exceptionally well compared to other methods.  \nKeywords: artificial neural network; dandelion optimizer; gold rush optimizer; peak load; forecast; support vector regression; particle swarm optimization  \n1. Introduction  \nEstimating energy needs involves addressing various prediction challenges within the utility sector. These challenges include forecasting demand, generation, prices, and power load across different timeframes and capacities [1] . Electricity plays a pivotal role asthe primary energy source for powering industries and enabling modern life. Electricity demand is closely intertwined with economic and population growth. Ensuring the stability of electricity supply requires accurate planning of electricity generation capacity, which, in turn, necessitates reliable electricity load f","cbCaic64eqYWzrF3","https://ap.wps.com/l/cbCaic64eqYWzrF3","pdf",2251291,1,22,"English","en",105,"# Introduction\n## Importance of electricity load forecasting\n## Short-term vs long-term forecasting goals\n# Methodology\n## Input selection and multilinear regression\n## Optimization and machine learning models\n# Evaluation\n## Error metrics and model comparison\n## Correlation analysis and predictive performance","[{\"question\":\"Why is instantaneous electricity peak load forecasting important?\",\"answer\":\"It supports efficient capacity planning and helps reduce risks and costs related to power system operation by improving how generation and dispatch are scheduled.\"},{\"question\":\"What data and inputs are used to forecast the peak load?\",\"answer\":\"The study uses input data from 1980 to 2020, including import/export data, population, and GDP, to forecast instantaneous electricity peak load.\"},{\"question\":\"Which models performed best according to the error metrics?\",\"answer\":\"The ANN and GRO approaches produced the least statistical errors, outperforming other compared methods under the evaluated metrics.\"}]","Instantaneous Electricity Peak Load Forecasting Using Optimization and Machine Learning | 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is instantaneous electricity peak load forecasting important?","Question",{"text":75,"@type":76},"It supports efficient capacity planning and helps reduce risks and costs related to power system operation by improving how generation and dispatch are scheduled.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and inputs are used to forecast the peak load?",{"text":80,"@type":76},"The study uses input data from 1980 to 2020, including import/export data, population, and GDP, to forecast instantaneous electricity peak load.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models performed best according to the error metrics?",{"text":84,"@type":76},"The ANN and GRO approaches produced the least statistical errors, outperforming other compared methods under the evaluated 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