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

Sustainable Built Environment Conference 2019 Tokyo Built Environment in an era of climate change: how can cities and buildings adapt?

生态环境科学

Fang, Chengkuan^1 ; Gao, Yuan^1 ; Ruan, Yingjun^1

Tongji University, China^1

关键词: Detection and diagnosis;    District energy systems;    Energy consumption datum;    Energy demand prediction;    Machine learning methods;    Meteorological parameters;    Predicted performance;    Transform decompositions;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/294/1/012031/pdf
DOI  :  10.1088/1755-1315/294/1/012031

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

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.