In the realm of positive building design, local energy production systems are gaining prominence. Strategies aimed at advancing this concept often involve optimizing hybrid renewable systems through various means, including sizing, maximizing power extraction, or energy management. Among these, energy management holds particular significance, especially in systems incorporating dual energy storage sources, as addressed in our work. Here, we explore the optimization of hybrid renewable systems, focusing on photovoltaic, wind, pumped storage, and battery storage as energy sources in a proposed hybrid local energy generation system. Managed by a multi-source controller, driven by an optimal energy management system, our approach aims to better fulfill the thermal needs of buildings in semi-arid climates. The system’s modeling and calculation processes are conducted within the TRNSYS environment. Utilizing TRNSYS embedded computers, the controller is programmed according to a tailored energy management algorithm. Simulation results demonstrate the system’s capability to effectively meet energy demands by leveraging renewable energy sources. Notably, our system showcases a remarkable 39% improvement in energy self-sufficiency compared to conventional approaches. Future research endeavors will explore the integration of high-resistivity phase change materials to further enhance positive building design.

The objective of energy transition is to convert the worldwide energy sector from using fossil fuels to using sources that do not emit carbon by the end of the current century. In order to achieve sustainability in the construction of energy-positive buildings, it is crucial to employ novel approaches to reduce reliance on fossil fuels. Hence, it is essential to develop buildings with very efficient structures to promote sustainable energy practices and minimize the environmental impact. Our aims were to shed some light on the standards, building modeling strategies, and recent advances regarding the methods of control utilized in the building sector and to pinpoint the areas for improvement in the methods of control in buildings in hopes of giving future scholars a clearer understanding of the issues that need to be addressed. Accordingly, we focused on recent works that handle methods of control in buildings, which we filtered based on their approaches and relevance to the subject at hand. Furthermore, we ran a critical analysis of the reviewed works. Our work proves that model predictive control (MPC) is the most commonly used among other methods in combination with AI. However, it still faces some challenges, especially regarding its complexity.