Long Short-Term Memory Network-Based Metaheuristic for Effective Electric Energy Consumption Prediction
Source abstract
Abstract
The Electric Energy Consumption Prediction (EECP) is a complex and important process in an intelligent energy management system and its importance has been increasing rapidly due to technological developments and human population growth. A reliable and accurate model for EECP is considered a key factor for an appropriate energy management policy. In recent periods, many artificial intelligence-based models have been developed to perform different simulation functions, engineering techniques, and optimal energy forecasting in order to predict future energy demands on the basis of historical data. In this article, a new metaheuristic based on a Long Short-Term Memory (LSTM) network model is proposed for an effective EECP. After collecting data sequences from the Individual Household Electric Power Consumption (IHEPC) dataset and Appliances Load Prediction (AEP) dataset, data refinement is accomplished using min-max and standard transformation methods. Then, the LSTM network with Butterfly Optimization Algorithm (BOA) is developed for EECP. In this article, the BOA is used to select optimal hyperparametric values which precisely describe the EEC patterns and discover the time series dynamics in the energy domain. This extensive experiment conducted on the IHEPC and AEP datasets shows that the proposed model obtains a minimum error rate relative to the existing models.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Load and Power Forecasting · Smart Grid Energy Management · Energy Efficiency and Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information Technology, Chameli Devi Group of Institutions, Indore 452020, India
Department of Computer Science and Engineering, New Horizon College of Engineering, Bangalore 560103, India
Department of Telecommunication Engineering, University of Jaén, 23700 Linares (Jaén), Spain
Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
Department of Telecommunication Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru 570016, India
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