Improving forecasting accuracy of daily energy consumption of office building using time series analysis based on wavelet transform decomposition

Abstract

In order to improve the operation, detection and diagnosis of district energy systems, it is necessary to develop energy demand prediction models. Several models for energy prediction have been proposed, including machine learning methods and time series analysis methods. Data-driven machine learning methods fail to achieve the expected accuracy due to the lack of measurement data and the uncertainty of weather forecasts, additionally it is not easy to obtain complete and long-term weather data sets of building as input data in China. In this case, a WT-ARIMA prediction model that combines wavelet transform and time series analysis without meteorological parameters can be a better choice. The predicted performance of the commonly used time series model, WT-ARIMA model and LSTM model was tested based on the energy consumption data for one year. The results show that the model proposed in this paper has a 20% accuracy improvement over the ARIMA model and can reduce data requirement with good forecasting accuracy compared with LSTM-h.