Print Email Facebook Twitter Partial Hierarchy Appliance Modelling In Household Energy Consumption Title Partial Hierarchy Appliance Modelling In Household Energy Consumption: Utilizing ARMA based methods to improve the prediction of household energy consumption Author Kienhuis, Maurits (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Kuilman, S.K. (mentor) de Weerdt, M.M. (graduation committee) Cavalcante Siebert, L. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-28 Abstract The ever-evolving power grid is becoming smarter and smarter. Modern houses come with smart meters and energy conscious consumers will buy additional smart meters to place in their home to help monitor their energy consumption. This new smart technology also opens the door to more accurate power consumption forecasting. In this study we look at utilizing a partial hierarchy, in which one of the appliances in a household is modelled separately from the rest of the house, to help improve household energy consumption forecasting accuracy. This is done in conjunction with Auto Regressive Moving Average (ARMA) based models. Three variants of ARMA based models will be looked at: Auto Regressive Integrated Moving Average (ARIMA), Seasonal Auto Regressive Integrated Moving Average (SARIMA), and Auto Regressive Integrated Moving Average with Exogenous variables (ARIMAX). These methods will then be compared to more baseline approaches such as a persistence method and a seasonal moving average. Our analysis has led us to conclude that the partial hierarchy model offers little to no benefit when applied in the field of household energy consumption forecasting when built upon ARMA based models. ARMA based models in general appeared to be poor performers when it came to household energy consumption forecasting. Subject Time Series ForecastingARIMAenergy consumption forecastinghousehold energy consumption To reference this document use: http://resolver.tudelft.nl/uuid:da0e7461-3263-4397-8aa0-30828d5ad65d Part of collection Student theses Document type bachelor thesis Rights © 2023 Maurits Kienhuis Files PDF BEP_8.pdf 384.16 KB Close viewer /islandora/object/uuid:da0e7461-3263-4397-8aa0-30828d5ad65d/datastream/OBJ/view