A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups

Deep learning-based models are ideally suited for accurately predicting electrical load in a smart grid. However, the computational overhead in training models and identifying optimal training hyperparameters are challenging problems. Two important challenges are addressed in this study. Firstly, a novel technique is proposed to accelerate the model training process by selecting optimal mini-batch size. The second challenge encountered is maintaining and operationalizing individual consumption forecasting models as they are not easily scalable. A methodology is proposed to generate deep learning-based models capable of forecasting energy consumption simultaneously for large user groups. The proposed methodology was empirically validated on data from 4710 individual users in ISSDA CER Dataset (approximately 25,632 datapoints/user) and 11 Feeder dataset data from NYISO (approximately 106163 datapoints/feeder). Eight deep learning algorithms trained using eleven mini-batch combinations, for two forecasting horizons resulted in 176 workflows. Training on 167 sample CER ISSDA user data resulted in 24,288 models and 75,360 individual models for the optimal mini batch. Similarly, 1,936 models were generated for the 11 NYISO Feeder datasets. A clustering approach is employed to group users with similar consumption patterns resulting in 107 models. Effectiveness of models is validated with eight deep learning algorithms trained for two forecasting horizons. Results demonstrate a 93.74% improvement on an average in convergence compared to conventional mini-batch sizes, achieving one among top three accuracy across various prediction horizons and user categories. This underscores the importance of tailored mini-batch selection methods for effective energy forecasting and management in Smart Grid systems.