Online Hour-Ahead Load Forecasting Using Appropriate Time-Delay Neural Network Based on Multiple Correlation–Multicollinearity Analysis in IoT Energy Network

To meet up fluctuations of the real-time electric load demands, many electricity markets have gone for the real-time-market-based operation. To do so, online forecasting of the real-time load demand is necessary. Due to changes in the relation between impacting variables and output over time, a continuous-learning-based approach is highly desired. A fixed data set-based training may perform accurately for a certain amount of time, but as the load pattern and impact of different external variables change, the performance of such a model may decrease. Thus, to overcome such an inherent problem of fixed-sized databased forecasting model development, in this work, a novel simultaneous online learning and feature-engineering-based appropriate time-delay neural network has been proposed. The data for developing a forecasting model are collected through different sensors available in the Internet of Things (IoT)-based networks. To develop an optimal-cost-effective IoT network and a parsimonious model for load forecasting, variable type-dependent correlation considering the multicollinearity has been performed in online training. The proper choice of the model has been also proved using the numerical analysis with the help of time-delay embedding theory. Interestingly, it is found that, with the proper choice of inputs and their lagged variables, the proposed model performs better over general feedforward, general regression neural networks, and several deep learning and advanced models, including recurrent neural networks, fully connected deep neural networks, and dendritic neuron model.