About this Research Topic
It is a current trend that all new buildings are constructed in a way that simultaneously reduces primary energy consumption (energy from utility grids) and increases their share of energy from Renewable Energy Sources (RES). The use of electrical storage has significantly raised the interest of both academics and professionals, as the levelized cost of electricity has been rapidly decreasing due to newer, more efficient and cheaper technologies; which to some extent can compete with conventional generation units. Moreover, it has been shown that buildings integrating electrical storage can further reduce electricity bills and at the same time increase the self-consumption of onsite Renewable Energy Generation (REG).
This Research Topic will serve as a guide to specific problems of Building Energy Management. It will include the presentation of ideas, methods and results related to energy optimization. We seek to attract a variety of studies related to convex mathematical optimization or any other technique for reducing energy consumption in buildings, e.g., in real-time and through storage, with or without integrated RES. Techniques such as Genetic Algorithms, Artificial Neural Networks, Machine Learning can be integrated to this end.
This Research Topic will refer to the development of new mathematical methods and computational algorithms and the application of new or existing methods in problem-solving in the field under study. Experimental studies in combination with numerical/computational work are expected. Assessing the accuracy of computational solutions through verification and validation are essential aspects to be dealt with. Review papers on relevant topics are also invited.
Themes relevant to this Research Topic include, but are not limited to, the following:
• Optimization of the integration of RES in buildings
• Optimized dispatch of battery energy for managing energy consumption profiles within buildings
• Achieving energy targets of nearly Zero Energy Buildings (nZEBs), using RES and battery, in real-time, once the building is inhabited and used
• Demand Side Management (DSM) and Demand Response (DR) for managing the nZEBs' energy, in real-time, once the building is inhabited and used
This Research Topic will serve as a guide to specific problems of Building Energy Management. It will include the presentation of ideas, methods and results related to energy optimization. We seek to attract a variety of studies related to convex mathematical optimization or any other technique for reducing energy consumption in buildings, e.g., in real-time and through storage, with or without integrated RES. Techniques such as Genetic Algorithms, Artificial Neural Networks, Machine Learning can be integrated to this end.
This Research Topic will refer to the development of new mathematical methods and computational algorithms and the application of new or existing methods in problem-solving in the field under study. Experimental studies in combination with numerical/computational work are expected. Assessing the accuracy of computational solutions through verification and validation are essential aspects to be dealt with. Review papers on relevant topics are also invited.
Themes relevant to this Research Topic include, but are not limited to, the following:
• Optimization of the integration of RES in buildings
• Optimized dispatch of battery energy for managing energy consumption profiles within buildings
• Achieving energy targets of nearly Zero Energy Buildings (nZEBs), using RES and battery, in real-time, once the building is inhabited and used
• Demand Side Management (DSM) and Demand Response (DR) for managing the nZEBs' energy, in real-time, once the building is inhabited and used
Keywords: Building Energy Optimization, nearly Zero Energy Buildings, Electrical Energy Storage, Linear Programming, Artificial Neural Networks, Genetic Algorithm, Convex optimization, Photovoltaics, Demand Side Management, Building Management Systems
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
