Artificial intelligence in energy industry: forecasting electricity consumption through cohort intelligence & adaptive neural fuzzy inference system

Demand forecasting is critical for energy systems, as energy is difficult to store and should only be supplied as needed. Researchers attempted to improve forecasts of energy consumption. However, they assume independent factors increase at a constant growth rate, which is unrealistic. Existing methods are designed to determine annual consumption, whereas energy-planning organizations rely on short- or medium-term consumption values. Therefore, we propose a new forecasting framework that introduces new models and scenarios. We apply a cohort intelligence-based adaptive neuro-fuzzy inference system (CI-ANFIS) with a subtractive clustering and grid partition approach to forecast net electricity consumption. One challenge in accurately predicting electricity consumption for specific projection intervals is missing values for factors independent of those known for existing net consumption. Then, we utilize a regression equation scenario approach. We test our framework using a real-world energy consumption dataset and show that our proposed framework outperforms the existing methods.