Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques

• We use an entropy-based feature selection to select relevant past consumptions.
• We compare the prediction power of one ML (Machine Learning), two SC (Soft Computing) and one Statistical technique.
• Performance of FIR (Fuzzy Inductive Reasoning) is higher than the other methodologies.
• FIR models are synthesized, which speeds up the modelling phase.

Scientific community is currently doing a great effort of research in the area of Smart Grids because energy production, distribution, and consumption play a critical role in the sustainability of the planet. The main challenge lies in intelligently integrating the actions of all users connected to the grid. In this context, electricity load forecasting methodologies is a key component for demand-side management. This research compares the accuracy of different Machine Learning methodologies for the hourly energy forecasting in buildings. The main goal of this work is to demonstrate the performance of these models and their scalability for different consumption profiles. We propose a hybrid methodology that combines feature selection based on entropies with soft computing and machine learning approaches, i.e. Fuzzy Inductive Reasoning, Random Forest and Neural Networks. They are also compared with a traditional statistical technique ARIMA (AutoRegressive Integrated Moving Average). In addition, in contrast to the general approaches where offline modelling takes considerable time, the approaches discussed in this work generate fast and reliable models, with low computational costs. These approaches could be embedded, for instance, in a second generation of smart meters, where they could generate on-site electricity forecasting of the next hours, or even trade the excess of energy.