Data-Driven Approaches for Prediction and Classification of Building Energy Consumption

A recent surge of interest in the building energy consumption has generated a tremendous amount of energy data, which boosts the data-driven algorithms for broad application throughout industry. This chapter reviews the prevailing data-driven approaches used in building energy analysis under different archetypes and granularities including those for prediction (artificial neural networks, support vector machines, statistical regression, decision tree and genetic algorithm) and those for classification (K-mean clustering, self-organizing map and hierarchy clustering). To be specific, we introduce the fundamental concepts and major technical features of each approach, together summarizing its current R&D status and practical applications while pointing out existing challenges in their development for prediction and classification of building energy consumption. The review results demonstrate that the data-driven approaches, although they are constructed based on less physical information, have well addressed a large variety of building energy related applications, such as load forecasting and prediction, energy pattern profiling, regional energy-consumption mapping, benchmarking for building stocks, global retrofit strategies and guideline making etc. Significantly, this review refines a few key tasks for modification of the data-driven approaches in the contexts of application to building energy analysis. The conclusions drawn in this review could facilitate future micro-scale changes of energy use for a particular dwelling through appropriate retrofit in building envelop and inclusion of renewable energy technologies. They also pave an avenue to explore potential in macro-scale energy-reduction with consideration of customer demands. All these will be useful to establish a better long-term strategy for urban sustainability.