An Explainable Artificial Intelligence Approach to Forecasting Monthly Electricity Consumption

Recent years have seen instability in the electricity market and increasing energy demands in the world. Reliable electricity consumption forecasting is a helpful tool for consumers and other stakeholders to manage energy consumption more efficiently. This thesis forecasts the monthly electricity consumption of individual residential households in Sweden using five robust ensemble learning models. To address a gap in previous research, the interpretability of the monthly residential forecasts is enhanced using insights produced by the explainable artificial intelligence (XAI) method SHAP. A diverse set of features, including historical consumption, household attributes, weather data, and calendar information, is investigated for their contributions to the model output. Results show that the ensemble models are able to forecast the consumption with around 15% prediction error, although performance is highly impacted by extreme values for some households. The XAI analysis reveals that recent consumption is the most determining factor for the forecasts, while external factors have a more varied impact depending on model. Two external features that are influential across all models are sunshine amount and wind direction, while several other features show complex or weak relationships to model output. Furthering the research on interpretable forecasts is important for increasing end-users' trust in the forecasts, and subsequently promoting more efficient energy management.