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.
Highlights
Introduction published maps and institutional affilIn recent decades, the demand for electricity has been rising on a global scale due to the massive growth of electronic markets [1], the development of electrical vehicles [2], the use of heavy machinery equipment [3], technological advancements, and rapid population growth [4,5]
The proposed metaheuristics based on the Long Short-Term Memory (LSTM) network includes three major The proposed metaheuristics based on the LSTM network includes three major phases phases in Energy Consumption Prediction (EECP), namely, data collection (AEP and Individual Household Electric Power Consumption (IHEPC) datasets), data refinement (minin EECP, namely, data collection (AEP and IHEPC datasets), data refinement and consumption prediction transformation and standard transformation methods) and consumption prediction
A new metaheuristic based on the LSTM model is proposed for effective household EECP
Summary
Previous works in the area are reviewed in order to justify the contribution of the proposal and the selection of strategies considered for comparison in the experimental section. Xu et al [19] combined a Deep Belief Network (DBN) and linear regression techniques to predict time series data. Galicia et al [27] introduced an ensemble classifier by combining random forest, gradient boosted trees and decision trees to forecast big data time series. The evaluation results showed that the developed ensemble classifier performed well in time series data prediction compared to other models and individual ensemble models. The artificial intelligencebased techniques, such as CNN, GRU, multi head attentions, ANFIS, and the ensemble schemes, are extensively applied for energy forecasting and time series issues. The GRU technique obtained a better outcome in EECP related to conventional techniques, but it is ineffective in handling long-term time series data sequences, and it is historically dependent. Predict and handle the short-term and long-term dependencies in energy forecasting
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