A Robust Building Energy Pattern Mining Method and its Application to Demand Forecasting

With the current shift of the Netherlands energy
systems to reduce its carbon footprint, appropriate planning is
essential. However, obtaining accurate energy predictions become
increasingly difficult from coupled weather and user behavior
volatility. This work proposes a robust load pattern identification
method through clustering whilst assessing the benefit of the
attained information on enhancing accuracies of building energy prediction. A robust cross-validation is illustrated from assessing varying distance metrics and clustering algorithms, namely Euclidean and mahalanobis distances with Fuzzy C-Means and Agglomerative Hierarchical clustering. Leveraging nine identified and characterized patterns, a final indirect evaluation is realized with energy demand forecasting. The proposed data mining method provides a better understanding of the interactions between user behavior and future energy needs which can fundamentally impact strategic energy planning such as asset
management, and collaborative operations.