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This article experimentally studies the thermal performance of latent heat storage in a two-phase thermosyphon solar water heater, which utilizes the superior heat transfer characteristics of boiling and condensation, and eliminates drawbacks found in the conventional solar water heater. Experimental investigations are first conducted to study the thermal behavior of tricosane (paraffin wax 116), water, and sodium acetate (NaC[H.sub.3]COO x 3[H.sub.2]O) used as energy storage materials. The results indicate that tricosane provides many advantages to be the energy storage material in the latent heat storage system. This study also examines the functions of charge and discharge thermal behaviors in a two-phase thermosyphon solar water heater The results show that the system gives optimum charge and discharge performance under 40% alcohol fill ratio and with tricosane used as the energy storage material, and displays an optimum charge efficiency of 73% and optimum discharge efficiency of 81%. [DOI: 10.1115/1.2147588] Keywords: energy storage material, two-phase thermosyphon, solar water heater

This article presents an energy storage system, which can be readily integrated with the building structure. It stores heat supplied by solar energy via the two-phase closed loop thermosyphon to storage tank and releases stored heat in energy storage material via two-phase closed thermosyphon to the heat exchanger through the flow of transport fluid. The functions of such energy storage system have three operating modes, i.e., heat charge, heat discharge, and simultaneous charge and discharge. The thermal performance of the system with alcohol and water as working fluid is experimentally investigated. The results show that the storage system employing alcohol as working fluid in the loop thermosyphon provides better performance; the system gives optimum heat charge and discharge performance under 35-40% fill ratio, regardless whether the working fluid is water or alcohol. The system displays optimum charge efficiency of 73% and optimum discharge efficiency of 85% with alcohol as working fluid.

Summary: In this study, artificial intelligence (AI) control tools were developed to construct an AI implementation framework for energy saving for buildings. Although numerous AI studies related to energy conservation have been conducted, most of them have reported computing algorithms and control effects for single objects. This is the first study to use a framework to integrate five‐category AI control tools to execute three‐level building energy conservation; the three levels consist of equipment‐level control, facility‐level control, and whole building energy saving. Energy‐saving effects were tested in a real building. The complex three‐floor building primarily with a total area of 9072 m2 serves as an office space and a semiconductor production line. Seventy percent energy consumption comes from air conditioning system and motor power. Twenty percent is lighting system and the other 10% is plug power and office automation equipment. Before implementation, the yearly energy cost reached US$1004339. In 2018, an AI implementation framework was introduced to systematically deploy AI at the site. A total of 47.5%, 37%, and 36.9% of energy was saved at equipment, facility, and whole building levels; up to US$385203 was saved. These energy savings proved the feasibility of the implementation framework. Furthermore, unmet demands of AI studies were met, and an approach to fill the research gap is discussed. [ABSTRACT FROM AUTHOR]

Benchmarking the energy performance of buildings has received increasing attention as striving for energy efficiency through more effective energy management has become a major concern of governments. Various methods for classifying building energy performance have been developed, and the clustering technique is considered one of the best approaches. This paper proposes a method utilizing dynamic clustering to analyze the electricity consumption patterns of buildings to decide the optimal cluster number and allocate the buildings to corresponding clusters for energy benchmarking. For the evaluation of number of clusters, this article has employed the inter–intra clustering method with particle swarm optimization algorithm. The electricity consumption data were collected through an energy survey performed in 30 junior high schools in Taipei, Taiwan. In a traditional method, the 30 schools would be grouped into one same cluster and the energy benchmarking report an average value of 541.4 kWh/year per student. The proposed method that took different electricity consumption patterns of the schools into consideration produced more detailed results as follows: the optimal cluster number was 3 with an inter–intra index value of 0.708, and the energy benchmarking index of these three clusters read, respectively, 362, 512, and 851 kWh/year per student. Practical application : The study proposed an innovative dynamic clustering technique to decide the optimal cluster number and allocate the assessed buildings. The results showed that compared to a traditional approach that tended to group assessed buildings into one cluster, the proposed method was able to classify the buildings into three clusters for further benchmarking. This method can be used by governments and large corporations. For example, in Hong Kong, primary schools are grouped into one cluster for energy benchmarking. Using the proposed method can further classify primary schools into more clusters; benchmarking index can then be developed for each cluster. [ABSTRACT FROM AUTHOR]

Abstract: Ice-storage air-conditioning system, while known for its advantage of shifting power consumption at peak hours during the day to the nighttime, can increase both energy consumption and CO2 emission. The study adopts particle swarm algorithm to facilitate optimization of ice-storage air-conditioning systems and to develop optimal operating strategies, using minimal life cycle cost as the objective function. Increase in power consumption and CO2 emission triggered by the use of ice-storage air-conditioning system is also examined and analyzed. Case study is based on a typical air-conditioning system in an office building. Results indicate that, with proper parameters, particle swarm algorithm can be effectively applied to the optimization of ice-storage air-conditioning system. In addition, optimal capacity of the ice-storage tank can be obtained. However, the volume of power consumption and CO2 emission rises with the increase in ice-storage tank capacity. Consideration of additional costs of power consumption like carbon tax can therefore lead to changes in the optimal system configuration. [Copyright &y& Elsevier]

Energy saving is an important issue for multiple-chiller systems. Optimal chiller loading (OCL) in multiple-chiller systems has been investigated with many optimization algorithms to save energy. Particle swarm optimization (PSO) algorithm has been successful in solving this problem in some cases, but not in all. This study innovatively added a team evolution to the original particle swarm optimization algorithm, called team particle swarm optimization (TPSO). The TPSO enhances the effectiveness of original particle swarm optimization to better solve the OCL problem. The TPSO algorithm is composed of two evolutions: particle evolution and team evolution. The partial load ratio (PLR) of each operating chiller and the on-off state of each chiller are the particle evolution parameters and team evolution parameters, respectively. To evaluate the performance of the proposed method, this paper adopts three case studies so the results generated from the proposed algorithm TPSO, the original particle swarm optimization (PSO) and other recently published algorithms can be compared. In these three case studies, the optimal results generated by using TPSO algorithm are the same as those by other compared algorithms. In case 1 under 5717 RT and 5334 RT cooling load, the results generated using the TPSO are lower than those by the original PSO in the amounts of 63.35 and 79.33 kW, respectively. The results indicated that the TPSO algorithm not only enabled the optimal solution in minimizing energy consumption, but also demonstrated the best stability when compared to other algorithms. In conclusion, the presented TPSO algorithm is an efficient and promising new algorithm for solving the OCL problem. [ABSTRACT FROM AUTHOR]

This article describes an experimental investigation on the thermal characteristics of a thermal energy storage device. It utilizes the superior heat transfer characteristics of wickless heat pipes and eliminates drawbacks found in the conventional thermal storage tank. This study purports to examine the functions of a thermal energy storage device having three operating modes, i.e., charge, discharge, and simultaneous charge and discharge. Different heat transfer mechanisms are identified in the thermal energy storage device under different operating modes. The thermal performance with alcohol and water as working fluid are also explored in the thermal energy storage device. The results show that a thermal energy storage device employing alcohol as the working fluid provides better performance. The system gives optimum charge and discharge performance under 35%–40% fill ratio and displays optimum charge efficiency of 73% and optimum discharge efficiency of 85%. [ABSTRACT FROM AUTHOR]

Abstract: This study employs gradient method (GM) to solve economic dispatch of chiller plant (EDCP) problem. GM overcomes the flaw that with the Lagrangian multiplier (LM) method the system may not converge at low demand. In this study, the load balance constraint and the operating limit constraints of the chillers are fully accounted for. After analysis and comparison of the two cases studies, we are confident to say that this method not only solves the problem of convergence, but also produces results with high accuracy within a rapid timeframe. It can be perfectly applied to the operation of air-conditioning systems. [Copyright &y& Elsevier]