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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.

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]