6533b7dcfe1ef96bd127351a
RESEARCH PRODUCT
Forecasting Electricity Consumption and Production in Smart Homes through Statistical Methods
Adrian FloreaUgo FioreFrancesco PalmieriRadu ChisArpad Gellertsubject
Consumption (economics)Renewable Energy Sustainability and the EnvironmentComputer sciencebusiness.industryTBATSGeography Planning and DevelopmentPhotovoltaic systemElectricity predictionTransportationEnergy consumptionARIMAEnvironmental economicsEnergy management systemSmart housePhotovoltaic panelsWork (electrical)ARIMA; Electricity prediction; Energy management system; Photovoltaic panels; Smart house; TBATSProduction (economics)Autoregressive integrated moving averageElectricityEnergy management systembusinessCivil and Structural Engineeringdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2022-01-01 | Sustainable Cities and Society |