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Forecasting Time-Series Energy Data in Buildings Using an Additive Artificial Intelligence Model for Improving Energy
Ngoc-Son Truong1, Ngoc-Tri Ngo1, Anh-Duc Pham1
1Faculty of Project Management, The University of Danang-University of Science and Technology, 54 Nguyen Luong Bang, Danang, Vietnam.
Abstract:
Building energy efficiency is important because buildings consume a significant energy amount. The study proposed additive artificial neural networks (AANNs) for predicting energy use in residential buildings. A dataset in hourly resolution was used to evaluate the AANNs model, which was collected from a residential building with a solar photovoltaic system. The proposed AANNs model achieved good predictive accuracy with 14.04% in mean absolute percentage error (MAPE) and 111.98 Watt-hour in the mean absolute error (MAE). Compared to the support vector regression (SVR), the AANNs model can significantly improve the accuracy which was 103.75% in MAPE. Compared to the ANNs model, accuracy improvement percentage by the AANNs model was 4.6% in MAPE. The AANNs model was the most effective forecasting model among the investigated models in predicting energy consumption, which provides building managers with a useful tool to improve energy efficiency in buildings.
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