Search Results (2)

Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems’ performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI’s and Elsevier’s databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields. Full article

An integrated electrical and thermal residential renewable energy system consisting of solar thermal collectors, gas boiler, fuel cell combined heat and power, a photovoltaic system with battery, inverter, and thermal storage for a single-family house of Sonnenhaus standard is investigated with a model predictive controller (MPC). The main focus of this article is to define a multi-objective mathematical function, develop a coupled simulation framework for the nonlinear time-varying deterministic discrete-time problem of the energy system using TRNSYS and MATLAB. With the developed methodology, a sensitivity analysis of maximum optimization time, swarm (or population or mesh) size of a typical spring day and a typical summer day assuming a 100% accurate weather and load forecast with three different algorithms: particle swarm optimization (PSO), genetic algorithm (GA) and global pattern search (GPS) are analyzed. Finally, the obtained results are compared with a status quo controller. Results show that the PSO algorithm optimizer performs the best in this MPC for such a complex and time-consuming MPC model in both the spring day and the summer day. The obtained results show that the PSO with swarm size 50 in the selected typical spring day and the PSO with swarm size 40 in the selected summer day reduces the objective function’s fitness value from 413 to −177 within 6 h optimization time and from 1396 to 1090 in 4 h optimization time respectively. Full article