Forecasting Demand-side Flexibility of a Household with Dynamic Consumer Behavior Analysis

Demand-side flexibility is considered an efficient concept for enhancing grid stability and optimizing energy resources amidst increasing renewable energy integration and fluctuating consumption patterns. A challenging task in demand-side flexibility adaptation is to forecast its availability while considering consumer comfort to assist aggregators in efficient energy management. This paper proposes a novel machine learning-based approach to address this issue by quantifying the demand side flexibility of household appliances and utilizes this quantification to forecast next-day flexibility, incorporating dynamic consumer behavior analysis. To achieve this, various flexibility metrics are employed, and temporal and energy variations are analyzed across flexible appliances, enabling a better comprehension of their interaction with the environment. K-means clustering is used to analyze consumer behavior, and XG-Boost is applied to forecast flexibility. Accurate forecasting of the flexibility profile is required to avoid penalties for not meeting the contracted capacity and to minimize the risk of failing to provide flexibility reserve due to overestimation of production by the aggregator. The results show that the proposed approach can be used to forecast flexibility with high accuracy.