Reducing Building Performance Through Energy-Savings Techniques
Y. Sai Geethika1, N. Viswas2, D. Rajesh Babu3
1,2B. Tech Student, Department of EEE, GMR Institute of Technology, Rajam-532127, Andhra Pradesh, India
3* Assistant Professor, Department of EEE, GMR Institute of Technology, Rajam-532127, Andhra Pradesh, India
Email: yenuguthalageethika@gmail.com
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Abstract:
Energy Management Systems in Buildings (EMS-in-Bs) are vital for optimizing energy efficiency and management. This paper reviews various EMS-in-Bs designs, focusing on their capabilities in monitoring, estimating, and controlling energy usage. It reveals that systems focused on control and optimization achieve the highest energy savings, up to 30%, compared to estimation and prediction functions, which save around 10%. The study underscores the need for advancements in estimation and prediction to enhance efficacy. Additionally, it explores Fuzzy Cognitive Maps (FCMs), a soft computing technique integrating neural networks and fuzzy logic, in building automation. FCMs have shown promising results across sectors, including medicine, transportation, manufacturing, agriculture, the food industry, and energy. This paper presents simulation and experimental results from case studies in Southern Greece, demonstrating the application of FCMs in residential and commercial buildings. Moreover, it discusses software tools based on these applications, with plans for further integration to generate real-world data. This data is crucial for advancing future research aimed at transitioning buildings from high energy consumption to achieving Net-Zero Energy Buildings (NZEB). Insights from reviewed studies guide the ongoing development of EMS-in-Bs, addressing current challenges and future directions. Buildings account for approximately 40% of global energy consumption, driving significant interest in improving their energy efficiency. The paper critically reviews traditional and modern approaches to building automation, focusing on strategies that drive energy savings. By providing a comprehensive evaluation of function-specific EMS-in-Bs, this study serves as a resource for selecting systems best suited to specific energy management needs. It highlights that control-optimize EMSs-in-Bs deliver an average energy savings rate of 22.57%, while estimate-predict systems achieve 10%, emphasizing the importance of continuous innovation in this field.
Keywords:
Energy efficiency, building insulation, Energy-efficient windows, HVAC system optimization, Smart thermostats, Energy management systems, LED lighting, Solar panels, Passive design, High-performance building materials, Building automation systems, Energy-efficient appliances, Sustainable building design, Net-zero energy buildings.
