Bridging performance gaps by occupancy and weather data-driven energy prediction modeling using neural networks

2018-04-20Studies have shown that the prediction of energy usage by simulation program does not always match the actual energy usage. Assumptions about occupancy and use of weather data different from site conditions are few of the factors affecting the accuracy of prediction. Hence, real-time occupancy, sub-metering and weather data for an office building in downtown Los Angeles was measured. Using the measured data, two methods of prediction, calibration simulation and artificial neural network, were studied to predict end-use energy usage. Measured sub-metered and occupancy data was used to generate lighting, equipment, and occupancy profile that were used for calibrating the model. Results showed that when real-time occupancy was used the prediction were less accurate compared to when lighting and equipment profiles were used. This indicates that occupancy was not modeled accurately in the simulation program. Different neural network configuration and time series data with different time lag were studied to best predict end-use energy usage. The fan energy was best predicted with 60-minutes time lag considered. Finally, comparison of neural network with simulation program showed that neural network predictions were more accurate