Over the last years, a steady increase in both domestic electricity consumption and in the adoption of personal clean energy production systems has been observed worldwide. By analyzing energy consumption and production on photovoltaic panels mounted in a house, this work focuses on finding patterns in electrical energy consumption and devising a predictive model. Our goal is to find an accurate method to predict electrical energy consumption and production. Being able to anticipate how consumers will use energy in the near future, homeowners, companies and governments may optimize their behavior and the import and export of electricity. We evaluated the ARIMA and TBATS statistical prediction methods and compared them with other models on datasets from a household equipped with photovoltaics and an energy management system. The evaluation results have shown a mean absolute error of 73.62 Watts for the TBATS model, which is far better than the one obtained with neural forecasting methods.

Forecasting Electricity Consumption and Production in Smart Homes through Statistical Methods

Fiore U.;Palmieri F.
In corso di stampa

Abstract

Over the last years, a steady increase in both domestic electricity consumption and in the adoption of personal clean energy production systems has been observed worldwide. By analyzing energy consumption and production on photovoltaic panels mounted in a house, this work focuses on finding patterns in electrical energy consumption and devising a predictive model. Our goal is to find an accurate method to predict electrical energy consumption and production. Being able to anticipate how consumers will use energy in the near future, homeowners, companies and governments may optimize their behavior and the import and export of electricity. We evaluated the ARIMA and TBATS statistical prediction methods and compared them with other models on datasets from a household equipped with photovoltaics and an energy management system. The evaluation results have shown a mean absolute error of 73.62 Watts for the TBATS model, which is far better than the one obtained with neural forecasting methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4780490
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