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• W4316260408 abstract "Energy consumption forecasting is a hot field of research; despite the number of developed models, projecting electric consumption in residential buildings remains problematic owing to the significant unpredictability of occupant energy use behavior. Discovering the electricity consumption knowledge from the multi-dimensional data streams (MDDS) of electricity logs is a challenging research problem. We propose a novel electricity knowledge discovery model proposed from the MDDS using clustering and machine learning. Context-aware clustering with whale optimization algorithm (CAC-WOA) is proposed to discover the predictive features from the electricity MDDS and perform the predictions using WOA. The CAC-WOA consists of two phases context-aware group formation and a WOA-based machine learning predictive model. In the CAC algorithm, group formation using electricity contextual information to estimate the robust predictive features are proposed. Using such predictive features, the predictive model using the WOA-based artificial neural network (ANN) is built. The modified ANN technique using the WOA algorithm is used to reduce the error rates and improve the prediction accuracy. The experimental outcomes using publicly available electricity consumption datasets prove the efficiency of the CAC-WOA model. Overall prediction accuracy is improved by 3.27% and prediction time is reduced by 11.31% using CAC-WOA compared state-of-the-art solutions." @default.
• W4316260408 created "2023-01-15" @default.
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• W4316260408 date "2023-01-15" @default.
• W4316260408 modified "2023-10-14" @default.
• W4316260408 title "CAC-WOA: context aware clustering with whale optimization algorithm for knowledge discovery from multidimensional space in electricity application" @default.
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• W4316260408 doi "https://doi.org/10.1007/s10586-023-03965-4" @default.
• W4316260408 hasPublicationYear "2023" @default.
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