Building an intelligent seasonal time series model for forecasting building electricity load
Abstract
The building sector is a significant energy consumer, and its share of energy consumption is increasing because of urbanization. Forecasting the electricity load for improving building energy efficiency is imperative for reducing energy costs and environmental impacts. This study first builds a seasonal time-series model, then integrates it with IoT in the energy-predict systems. Notably, the built time-series model gives positive results with an R2 training of 0.814 and an R2 test of 0.803, which are much better than the regression model in accuracy and feature cost. Lastly, the proposed system automatically collects data from an IoT platform, predicts energy consumption, and sends results to end users. This system can help the user control their energy consumption or abnormal energy consumption in a home in real time.
Moraref