Electricity Forecasting Using Machine Learning: A Review

Pawar, Shital; Mole, Prajakta; Phadtare, Shweta; Aghor, Dhanashri; Vadtile, Pranali

doi:10.1007/978-981-33-4859-2_12

Advances in Intelligent Systems and Computing

2021

DOI: 10.1007/978-981-33-4859-2_12

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Electricity Forecasting Using Machine Learning: A Review

Shital Pawar¹,

Prajakta Mole²,

Shweta Phadtare³

et al.

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2023

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“…After comparison and analysis, the authors found that different machine learning algorithms have different levels of accuracy and stability in predicting electricity demand, and the accuracy of the model is also influenced by multiple factors. One study proposed a hybrid model based on wavelet transform and artificial intelligence technology [24] for hourly electricity demand prediction. The results showed that the model could improve the accuracy of electricity demand prediction.…”

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Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models

Yao,

Kao

2023

Applied Sciences

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The energy discourse is multifaceted, encompassing energy creation, storage, and conservation. Beyond the imperative of conserving energy consumption, effective energy management is a critical aspect of achieving overall energy efficiency. Despite being traditionally regarded as low electricity consumers, retailers play a pivotal role in economic activity. While categorized as non-productive energy users, the retail industry operates numerous establishments, facing substantial energy costs that make energy management integral to its operations. Historically, smaller retail stores have lacked awareness of energy saving. However, by connecting these stores, even modest reductions in individual electricity consumption can yield significant overall energy savings. This study aims to investigate the feasibility of implementing the demand-side management (DSM) aggregator model in the retail industry. Through surveys on awareness of energy saving and the application of deep learning techniques to analyze the effectiveness of the Aggregator model, the results reveal that the mean squared prediction error (MSPE) of this research is below 2.05%. This indicates substantial accuracy and offers meaningful reference value for Energy Service Company (ESCO) providers. The findings contribute practical recommendations for the sustainable and competitive implementation of DSM energy management practices in smart cities.

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Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models

Yao,

Kao

2023

Applied Sciences

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Electricity Forecasting Using Machine Learning: A Review

Cited by 1 publication

References 1 publication

Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models

Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models

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