Machine learning for performance prediction in smart buildings: Photovoltaic self-consumption and life cycle cost optimization

The application of Photovoltaic (PV) system in buildings is growing rapidly in response to the need for clean energy sources and building decarbonization targets. Nonetheless, enhancing PV self-consumption through technical solutions such as Energy Storage Systems (ESS) is getting higher importance to increase the profitability of PV plants, by minimizing the building-grid interaction. In this context, analyzing PV self-consumption of different energy storage configurations becomes more relevant and crucial in building energy modeling although it is heavily time-consuming and complicated, particularly within a multi-objective optimization related to the ESS design. As a solution to resolve this issue, this paper evaluates the accuracy, training, and prediction speed of 24 Machine Learning (ML) models to be used as surrogate models for analyzing PV self-consumption in smart buildings. Furthermore, the performance of short-term Thermal Energy Storage (TES) to increase PV self -consumption is assessed and presented using ML models. The results showed the Gaussian Process Regression (GPR), Neural Networks (NN) including bilayered and trilayered NN models, Support Vector Machines (SVM) including the fine gaussian and cubic SVM models, and Ensembles of Trees (EoT) as superior ML models. The results also revealed that TES systems can efficiently increase PV self-consumption in the building equipped with electric heat pumps to provide heating, cooling, and domestic hot water. Moreover, the TES size optimization regarding the Life Cycle Cost (LCC) showed that the LCC-based optimum TES size can yield 7.1% savings within 30 years of the building service life. The novelties of this research are first to provide a reference to select the most suitable ML models in predicting PV self-consumption, second to implement machine learning for analyzing the performance of short-term thermal energy storage to enhance PV self-consumption in buildings, and third to carry out an LCC-based optimization on TES size using ML-based prediction models.