Modeling and Forecasting of Industrial Consumers’ Load Using Fuzzy Clustering and Advanced Neural Networks

Modeling and Forecasting of Industrial Consumers’ Load Using Fuzzy Clustering and Advanced Neural Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modeling and Forecasting of Industrial Consumers’ Load Using Fuzzy Clustering and Advanced Neural Networks SeyedHamed MirMohammadAli Roudaki, Amin Helmzadeh, Aliakbar Hajnoouzi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7574518/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Effective electrical load management is essential for enhancing power system stability and minimizing energy costs. This study presents a methodology for analyzing and forecasting electricity consumption patterns. Initially, consumption data were examined using the Fuzzy C-Means (FCM) clustering algorithm. The fuzzy approach was chosen for its ability to handle uncertainties and overlapping data, allowing for the assignment of membership degrees to each cluster and providing a more precise analysis of complex consumption patterns. Cluster centers, representing typical consumption behaviors, were subsequently used for future load forecasting employing two advanced deep learning models: Long Short-Term Memory (LSTM) and Bidirectional Temporal Convolutional Network (BTCN). Evaluation based on RMSE and MAE metrics indicated that BTCN, leveraging its bidirectional structure and enhanced temporal learning capabilities, outperformed LSTM in prediction accuracy. This methodology offers a powerful tool for load management and the design of optimal electricity consumption strategies. Finally, suggestions for further research and potential improvements are discussed. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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