The energy performance of buildings has become a main concern globally in response to increased energy demand, the environmental impacts of energy production, and the reality of energy poverty. To improve energy efficiency, proper building design should be secured at the early design phase. Digital tools are currently available for performing energy assessment analyses and can efficiently handle complex and technically demanding buildings. However, alternative designs should be checked individually, and this makes the process time-consuming and prone to errors. Machine learning techniques can provide valuable assistance in developing decision support tools. In this paper, typical residential buildings are considered along with eleven factors that highly affect energy performance. A dataset of 337 instances of such parameters is developed. For each dataset, the building energy performance is estimated based on BIM analysis. Next, statistical and machine learning techniques are implemented to provide artificial models of energy performance. They include statistical regression modeling (SRM), decision trees (DTs), random forests (RFs), and artificial neural networks (ANNs). The analysis reveals the contribution of each factor and highlights the ANN as the best performing model. An easy-to-use interface tool has been developed for the instantaneous calculation of the energy performance based on the independent parameter values.

Risk management has become an important concern in the light of current developments in the home energy management sector as well as within the broader considerations regarding the building sector’s energy production and consumption paradigm. The current multi-parameter energy ecosystem structure raises a number of new challenges that require a reliable and robust risk management framework to assist in building management decision making. This paper presents a multi asset risk assessment algorithm, which is part of a risk management application developed for residential buildings within the framework of energy communities and digital energy markets. It describes the logic, principles, and operation of the algorithm, as well as the functionalities related to risk analysis and result visualization. This underpins the necessary means to monitor elements of a home energy system as well as tools for risk prevention and mitigation. The proposed application provides accurate, detailed, and easy to use information to assist decision makers and stakeholders in the context of smart home energy management systems.