Energy consumption prediction by using machine learning for Shenzhen Civic Center

Case Study Feb 15, 2022

Building Energy Management System has been a hot topic nowadays as of its importance in reducing energy wastage and carbon emissions. However, the performance of building energy consumption prediction has been stagnant due to problems such as low prediction accuracy.

With recent year’s Internet of Things technology has developed rapidly, and the sensor device price become economic, now we can collect the building energy related data from variable sensors and smart meters. it makes a good fit for the machine learning to entering the building energy prediction space.

Recently we have successfully completed a machine learning energy prediction case for Shenzhen Civic Center with energy consumption data from building operation team support.


In this case study, a time-series prediction model based on Prophet was modified and used for the Shenzhen Civic Center energy consumption prediction.

Essentially, it is Time Series forecasting, which is a method to arrange the historical data of the forecast target into a time series, and then analyze its development trend over time, and extrapolate the future value of the forecast target.

A major advantage of using this modified Prophet model is, it can quickly adapt to the yearly trends, the season and holiday factors in one model.

We have analyzed the data of the civic center in the past two years and conducted predictive learning to predict the hourly energy consumption for the coming three months. After comparing with the actual consumption, the result is exciting.


The machine learning energy consumption prediction model has been deployed to the cloud, provides a rolling three monthly energy consumption forecast to the building operator continuously, helps the building management team to:

  • foresee the energy consumption trend
  • adjust the energy equipment maintenance schedule
  • reduce the energy supply disruption

With the study case, it also provides the solid base to optimize the IoT network for further smart building initiatives of Civic Center.