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Predictive Analytics for Energy Efficiency: Leveraging Machine Learning to Optimize Household Energy Consumption

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  • Piotr Powroźnik

    (Institute of Metrology, Electronics and Computer Science, University of Zielona Góra, 65-516 Zielona Góra, Poland)

  • Paweł Szcześniak

    (Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, Wincentego Pola 2, 35-959 Rzeszów, Poland)

Abstract

This paper presents a novel machine learning framework useful for optimizing energy consumption in households. Home appliances have a great potential to optimize electricity consumption by mitigating peaks in the grid load or peaks in renewable energy generation. However, such functionality of home appliances requires their users to change their behavior regarding energy consumption. One of the criteria that could encourage electricity users to change their behavior is the cost of energy. The introduction of dynamic energy prices can significantly increase energy costs for unsuspecting consumers. In order to be able to make the right decisions about the process of electricity use in households, an algorithm based on machine learning is proposed. The presented proposal for optimizing electricity consumption takes into account dynamic changes in energy prices, energy production from renewable energy sources, and home appliances that can participate in the energy optimization process. The proposed model uses data from smart meters and dynamic price information to generate personalized recommendations tailored to individual households. The algorithm, based on machine learning and historical household behavior data, calculates a metric to determine whether to send a notification (message) to the user. This notification may suggest increasing or decreasing energy consumption at a specific time, or may inform the user about potential cost fluctuations in the upcoming hours. This will allow energy users to use energy more consciously or to set priorities in home energy management systems (HEMS). This is a different approach than in previous publications, where the main goal of optimizing energy consumption was to optimize the operation of the power system while taking into account the profits of energy suppliers. The proposed algorithms can be implemented either in HEMS or smart energy meters. In this work, simulations of the application of machine learning with different characteristics were carried out in the MATLAB program. An analysis of machine learning algorithms for different input data and amounts of data and the characteristic features of models is presented.

Suggested Citation

  • Piotr Powroźnik & Paweł Szcześniak, 2024. "Predictive Analytics for Energy Efficiency: Leveraging Machine Learning to Optimize Household Energy Consumption," Energies, MDPI, vol. 17(23), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5866-:d:1527285
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    References listed on IDEAS

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    1. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
    2. Freier, Julia & von Loessl, Victor, 2022. "Dynamic electricity tariffs: Designing reasonable pricing schemes for private households," Energy Economics, Elsevier, vol. 112(C).
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