Why does Building Energy Optimization fail and how to avoid it?

Significance 

The fast growth in population is equally responsible for the increased carbon footprint we observe today. To be precise, there exist a direct proportionality relationship between population and resource utilization. Among various resources, the building sector has become extremely energy intensive; statistically being responsible for approximately 39% of the primary energy used worldwide. In this scenario, building designs that emphasize energy efficiency are therefore significant for achieving energy conservation and reducing negative environmental impact.

As shown in several important review works, a new technique known as building energy optimization (BEO) has become a very active research field credit to its ability to simulate energy consumption and automatically search for the best energy efficient design. Algorithms employed in this technique are critical and as the authors stated “they are the heat and soul”. Algorithms can be grouped into three: direct search algorithms, intelligent optimization algorithms, and hybrid algorithms.

Research has shown that these algorithms play a critical role in determining the effectiveness and efficiency of BEO techniques. Nonetheless, an effective optimization algorithm should simultaneously meet three criteria as outlined in the original work referenced here. However, it is rather unfortunate that the BEO optimization algorithm is vulnerable to being ineffective.

In this context, Southeast University researchers: Binghui Si (PhD candidate), Zhichao Tian (PhD candidate), Dr. Xing Jin, Dr. Xin Zhou and led by Professor Xing Shi studied carefully the reasons for the ineffectiveness in the popular building energy optimization technique. In particular, they focused on assessing and hopefully adequately addressing inherent problems in the stated technique, such as; firm definition of algorithm ineffectiveness, the reasons that cause an algorithm to be ineffective, and its risk of becoming ineffective. Their work is currently published in the research journal, Renewable Energy.

In brief, to investigate the causes of ineffectiveness, four commonly used optimization algorithms were selected and applied to a representative BEO problem to perform numerical experiments. Specifically, they began with a systematic definition of optimization algorithms’ ineffectiveness, describing five ineffective scenarios. Then, a reference building and a representative energy optimization problem were proposed. Four commonly used optimization algorithms, namely, discrete Armijo gradient, Hooke-Jeeves, particle swarm optimization with constriction coefficient and particle swarm optimization with inertia weight, were then tested to determine the circumstances and the causal factors under which they become ineffective.

The authors reported that the effectiveness of the discrete Armijo gradient algorithm was dependent on its control parameters and the position of the initial solution in the design space. Additionally, for the Hooke-Jeeves algorithm, the position of the initial solution in the design space was observed to be crucial to the effectiveness of the algorithm because of its poor ability to escape from local optima.

In summary, the study Professor Xing Shi and his research team focused on addressing whether different algorithm control parameter settings and different initial solutions could cause the four most common algorithms to be ineffective when used in BEO. For this purpose, four sets of different control parameter settings and four different initial solutions were randomly chosen for each test. Overall, the research presented by Southeast University scientists shed more light on the performance of algorithms in BEO and revealed their failure mechanisms. The results can be very helpful to architects, engineers, and designers who use BSO and want to avoid ineffective algorithms. Since BEO is an emerging design technique for green buildings, the green building design in general can also benefit significantly from the results.

Why does Building Energy Optimization fail and how to avoid it? - Advances in Engineering Why does Building Energy Optimization fail and how to avoid it? - Advances in Engineering Why does Building Energy Optimization fail and how to avoid it? - Advances in Engineering

About the author

Xing Shi, Ph.D., LEED A.P., professor at the School of Architecture, Southeast University, China.

Dr. Shi received his bachelor degree from Tongji University, China and this Ph.D. degree from the Pennsylvania State University, the USA. From 2005 to 2008, he worked at Walter P Moore in Houston, the USA as a consulting engineer. In 2008, he joined the faculty of the School of Architecture, Southeast University, China.

His research interests include building performance and design optimization, green buildings, urban energy simulation, energy efficient urban form, etc. He is the PI of more than ten research projects, including those funded by the National Natural Science Foundation and the Ministry of Science and Technology. He has extensive design and consulting experience on some high-profile projects. He has published more than 80 papers and 4 books and holds 16 patents and software copyrights.

Dr. Shi is the associate editor-in-chief of Frontiers of Architectural Research. He serves on the board of several international journals and professional associations. He can be reached at [email protected].

About the author

Binghui Si is currently a Ph.D. candidate in School of Architecture at Southeast University, Nanjing, China. She received her Bachelor and Master degrees in Architecture from Nanjing University in 2012 and 2014, respectively. From December 2017 to December 2018, she studied at the University of California, Berkeley as a visiting scholar for one year.

Since 2015, she has been working on building energy simulation and optimization, especially focusing on the performance of algorithms used in building energy optimization. Until now, she has published nine journal papers and has participated in four research projects, including those funded by the National Natural Science Foundation and the Ministry of Science and Technology. She has delivered presentations in several prestigious international conferences such as the 3rd IBPSA Asia Conference, Building Simulation 2017 and 7th International Building Physics Conference (IBPC2018).

Reference

Binghui Si, Zhichao Tian, Xing Jin, Xin Zhou, Xing Shi. Ineffectiveness of optimization algorithms in building energy optimization and possible causes. Renewable Energy, volume 134 (2019) page 1295-1306.

Go To Renewable Energy

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