A hybrid CNN-GRU based probabilistic model for load forecasting from individual household to commercial building

Abstract

In load forecasting, build load is relatively difficult to predict due to its variability and uncertainty. This study intends to develop a hybrid deep learning models and quantile regression loss function for building load prediction. Then, through the numerical study for the individual and commercial building, it is provided to demonstrate the practicality of proposed method which showed the accuracy with lower loss than existing models. In academic contribution, this study proves that Convolutional Neural Network (CNN) can extract useful information from high uncertainty power load, and Gated Recurrent Unit (GRU) has the benefit in the times-series forecasting. Furthermore, the proposed hybrid framework outperforms the conventional LSTM and GRU neural network. In practical contribution, the issues of reducing energy waste and demand side management can be improved through more accurate load forecasting. Also, RE100 company can realize the risk of the power purchasing planning and customized decision-making through prediction intervals.

Keywords

• Deep learning
• Hybrid model
• Energy forecasting
• Building load
• Prediction interval