Heating and cooling energy requirements for buildings are usually supplied by separated systems such as furnaces or boilers for heating, and vapor compression systems for cooling. For these types of buildings, the use of combined cooling, heating, and power (CCHP) systems or combined heating and power (CHP) systems are an alternative for energy savings. Different researchers have claimed that the use of CCHP and CHP systems reduces the energy consumption related to transmission and distribution of energy. However, most of these analyses are based on reduction of operating cost without measuring the actual energy use and emissions reduction. The objective of this study is to analyze the performance of CCHP and CHP systems operating following the electric load (FEL) and operating following the thermal load (FTL), based on primary energy consumption (PEC), operation cost, and carbon dioxide emissions (CDE) for different climate conditions. Results show that CCHP and CHP systems operated FTL reduce the PEC for all the evaluated cities. On the other hand, CHP systems operated FEL always increases the PEC. The only operation mode that reduces PEC and CDE while reducing the cost is CHP-FTL. Copyright © 2009 John Wiley & Sons, Ltd.

The building sector is one of the largest contributors to the world’s total energy use and greenhouse gas emissions. Advancements in building energy technologies have played a critical role in enhancing the energy sustainability of the built environment. Extensive research and new techniques in energy and environmental systems for buildings have recently emerged to address the global challenges. This study reviews existing articles in the literature, mostly since 2000, to explore technological advancement in building energy and environmental systems that can be applied to smart homes and buildings. This review study focuses on an overview of the design and implementation of energy-related smart building technologies, including energy management systems, renewable energy applications, and current advanced smart technologies for optimal function and energy-efficient performance. To review the advancement in building energy-related technologies, a systematic review process is adopted based on available published reviews and research types of articles. Review-type articles are first assessed to explore the current literature on the relevant keywords and to capture major research scopes. Research-type papers are then examined to investigate associated keywords and work scopes, including objectives, focuses, limitations, and future needs. Throughout the comprehensive literature review, this study identifies various techniques of smart home/building applications that have provided detailed solutions or guidelines in different applications to enhance the quality of people’s daily activities and the sustainability of the built environmental system. This paper shows trends in human activities and technology advancements in digital solutions with energy management systems and practical designs. Understanding the overall energy flow between a building and its environmentally connected systems is also important for future buildings and community levels. This paper assists in understanding the pathway toward future smart homes/buildings and their technologies for researchers in related research fields.

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.

The exergy topological method is used to present a quantitative estimation of the exergy destroyed in an organic Rankine cycle (ORC) operating on R113. A detailed roadmap of exergy flow is presented using an exergy wheel, and this visual representation clearly depicts the exergy accounting associated with each thermodynamic process. The analysis indicates that the evaporator accounts for maximum exergy destroyed in the ORC and the process responsible for this is the heat transfer across a finite temperature difference. In addition, the results confirm the thermodynamic superiority of the regenerative ORC over the basic ORC since regenerative heating helps offset a significant amount of exergy destroyed in the evaporator, thereby resulting in a thermodynamically more efficient process. Parameters such as thermodynamic influence coefficient and degree of thermodynamic perfection are identified as useful design metrics to assist exergy-based design of devices. This paper also examines the impact of operating parameters such as evaporator pressure and inlet temperature of the hot gases entering the evaporator on ORC performance. It is shown that exergy destruction decreases with increasing evaporator pressure and decreasing turbine inlet temperatures. Finally, the analysis reveals the potential of the exergy topological methodology as a robust technique to identify the magnitude of irreversibilities associated with real thermodynamic processes in practical thermal systems. Copyright © 2008 John Wiley & Sons, Ltd.

The increasing demand for electrical power as well as energy for heating and cooling of residences and small commercial buildings is a growing worldwide concern. Micro-cooling, heating, and power (micro-CHP), typically designated as less than 30 kW electric, is decentralized electricity generation coupled with thermally activated components for residential and small commercial applications. The number of combinations of components and parameters in a micro-CHP system is too many to be designed through experimental work alone. Therefore, theoretical models for different micro-CHP components and complete micro-CHP systems are needed to facilitate the design of these systems and to study their performance. This paper presents a model for micro-CHP systems for residential and small commercial applications. Some of the results that can be obtained using the developed model include the cost per month of operation of using micro-CHP versus conventional technologies, the amount of fuel per month required to run micro-CHP systems, the overall efficiency of micro-CHP systems, etc. A case study is used to demonstrate differences in the system performances of micro-CHP systems driven by a natural gas internal combustion engine and a diesel engine. Some of the results show that both systems have similar performance and that system total efficiencies in cooler months of up to 80% could be obtained. Also, modeling results show that there is a limit in fuel price that economically prevents the use of CHP systems, which is $11 MBTU⁻¹ for this specific case. Copyright © 2008 John Wiley & Sons, Ltd.

Building energy consumption accounts for about 40% of global primary energy use and 30% of worldwide greenhouse gas (GHG) emissions. Among the energy-related factors present in buildings, heating, cooling, and air-conditioning (HVAC) systems are considered major contributors to whole-building energy use. To improve the energy efficiency of HVAC systems and mitigate whole-building energy consumption, accurately predicting the building energy consumption can play a significant role. Although many prediction approaches are available for building energy use, a machine learning-based modeling approach (i.e., black box models) has recently been considered to be one of the most promising building energy modeling techniques due to its simplicity and flexibility compared to physics-based modeling techniques (i.e., white box models). This study presents a building energy load forecasting method based on long-term short-term memory (LSTM) and transfer learning (TL) strategies. To implement this approach, this study first conducted raw data pre-processing analysis to generate input datasets. A hospital building type was considered for a case study in the first stage. The hospital prototype building model, developed by the U.S. department of energy (DOE), was used to generate an initial input training and testing dataset for source domain tasks before the transfer learning process. For the transfer learning process in a target domain, a simulation-based analysis was also conducted to obtain target datasets by assuming limited data lengths in different weather conditions. The training and testing procedures were performed using separate cooling and heating periods with and without the transfer learning process for source and target domain tasks, respectively. Lastly, a comparative analysis was carried out to investigate how the accuracy of LSTM prediction can be enhanced with the help of transfer learning strategies. The results from this study show that the developed LSTM-TL model can achieve better performance than the prediction model, which only uses LSTM under different weather conditions. In addition, accurate performance can vary according to different transfer learning methods with frozen and fine-tuning layers and locations.