On the trade-off between profitability, complexity and security of forecasting-based optimization in residential energy management systems

With the emergence of affordable access to data sources, machine learning models and computational resources, sophisticated control concepts for residential energy management systems (EMSs) are on the rise. At the heart of those are production and consumption forecasts. Given the wide spectrum of implementation opportunities, selection of appropriate forecasting strategies is challenging. This work systematically evaluates forecasting-based optimization for residential EMSs in terms of trade-offs between economic profitability, computational complexity and security. The foundation of the study is two real prosumer cases equipped with a photovoltaic-battery system. Results demonstrate that, within the considered scenarios, best trade-offs are achieved based on forecasts of a default gradient-boosted decision trees model, using a short initial training set, weather forecast inputs and regular retraining. Over 90% of the theoretical maximum economic benefit is achieved in this scenario, at significantly lower computational complexity than others with similar savings, while being applicable to new systems without large data history. In terms of security, this scenario exhibits tolerance against weather input manipulation. However, sensitivity to price tampering may require data integrity checking in residential EMSs.