Hybrid deep learning models with data fusion approach for electricity load forecasting

This study explores the application of deep learning in forecasting electricity consumption. Initially, we assess the performance of standard neural networks, such as convolutional neural networks (CNN) and long short-term memory (LSTM), along with basic methods like ARIMA and random forest, on a univariate electricity consumption data set. Subsequently, we develop hybrid models for a comprehensive multivariate data set created by merging weather and electricity data. These hybrid models demonstrate superior performance compared to individual models on the univariate data set. Our main contribution is the introduction of a novel hybrid data fusion model. This model integrates a single-model approach for univariate data, a hybrid model for multivariate data, and a linear regression model that processes the outputs from both. Our hybrid fusion model achieved an RMSE value of 0.0871 on the Chicago data set, outperforming other models such as Random Forest (0.2351), ARIMA (0.2184), CNN (0.1802), LSTM + LSTM (0.1496), and CNN + LSTM (0.1587). Additionally, our model surpassed the performance of our base transformer model. Furthermore, combining the best-performing transformer model, with a Gaussian Process model resulted in further improvement in performance. The Transformer + Gaussian model achieved an RMSE of 0.0768, compared with 0.0781 for the single transformer model. Similar trends were observed in the Pittsburgh and IHEC data sets.