Criteria for identifying failure optimization algorithms in building energy optimization and case studies

Optimization algorithms plays a vital role in the Building Energy Optimization (BEO) technique. Although many algorithms are currently used in BEO, it is difficult to find an algorithm that performs well for all optimization problems. Some algorithms may fail in some cases. This study specifically focuses on failure algorithms in BEO and the possible causes. Several criteria are proposed for identifying failure algorithms. Four optimization problems base d on the DOE small and large office buildings are developed. Three commonly used algorithms in BEO, namely, Pattern Search (PS ) algorithm, Genetic Algorithm (GA ) and Particle Swarm Optimization (PSO) algorithm, are applied to the four problems to investigate possible rea sons for their failure. Results indicate that the effectiveness of the three selected algorithms is highly dependent on the optimization problems to be addressed. Besides, the control parameter setting of the PS algorithm appears to be a significant factor that may cause the algorithm to lose effectiveness. However, it does not seem to be the main reason for the failure of the GA and PSO algorithm. In General, the results gained from this study can deepen our understanding of optimization algorithms used in BEO. Besides, understanding the reasons why optimization algorithms are ineffective can help architects, engineers, and consultants select the appropriate optimization algorithms and set their parameters to achieve a better BEO design that is less vulnerable to failure

Metamodel-Based Hyperparameter Optimization of Optimization Algorithms in Building Energy Optimization

Building energy optimization (BEO) is a promising technique to achieve energy efficient designs. The efficacy of optimization algorithms is imperative for the BEO technique and is significantly dependent on the algorithm hyperparameters. Currently, studies focusing on algorithm hyperparameters are scarce, and common agreement on how to set their values, especially for BEO problems, is still lacking. This study proposes a metamodel-based methodology for hyperparameter optimization of optimization algorithms applied in BEO. The aim is to maximize the algorithmic efficacy and avoid the failure of the BEO technique because of improper algorithm hyperparameter settings. The method consists of three consecutive steps: constructing the specific BEO problem, developing an ANN-trained metamodel of the problem, and optimizing algorithm hyperparameters with nondominated sorting genetic algorithm II (NSGA-II). To verify the validity, 15 benchmark BEO problems with different properties, i.e., five building models and three design variable categories, were constructed for numerical experiments. For each problem, the hyperparameters of four commonly used algorithms, i.e., the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, simulated annealing (SA), and the multi-objective genetic algorithm (MOGA), were optimized. Results demonstrated that the MOGA benefited the most from hyperparameter optimization in terms of the quality of the obtained optimum, while PSO benefited the most in terms of the computing time

Performance Assessment of Algorithms for Building Energy Optimization Problems with Different Properties

Assessing the performance of algorithms in solving building energy optimization (BEO) problems with different properties is essential for selecting appropriate algorithms to achieve the best design solution. This study begins with a classification of the properties of BEO problems from three perspectives, namely, design variables, objective functions, and constraints. An analytical approach and a numerical approach are proposed to determine the properties of BEO problems. Six BEO test problems with different properties, namely, continuous vs. discrete, convex vs. non-convex, linear vs. non-linear, uni-modal vs. multimodal, and single-dimensional vs. multi-dimensional, are composed to evaluate the performance of algorithms. The selected optimization algorithms for performance assessment include the discrete Armijo gradient, Particle Swarm Optimization (PSO), Hooke-Jeeves, and hybrid PSO and Hooke-Jeeves. The assessment results indicate that multimodality can cause Hooke-Jeeves and discrete Armijo gradient algorithms to fall into local optima traps. The convex, non-convex, linear and non-linear properties of uni-modal BEO problems have little impact on the performance behavior of the algorithms. The discrete Armijo gradient and Hooke-Jeeves are not recommended for solving discrete and multi-dimensional BEO problems

Magnitude, Causes, and Solutions of the Performance Gap of Buildings: A Review

The performance gap of buildings is commonly defined as the difference between the performance value predicted in the design stage and that measured in the post-occupancy stage. Knowledge about the performance gap of buildings is valuable in many aspects and thus is a research subject drawing much attention. Important questions that should be asked include: (1) Definition: what is the performance gap of buildings? (2) Magnitude: how large is the performance gap of buildings? (3) Techniques: how to determine the performance gap of buildings? (4) Causes: what are the reasons leading to the performance gap of buildings? (5) Solutions: how to bridge the performance gap of buildings. By collecting and analyzing more than 20 published works with reported data on the performance gap of buildings and other research articles, these important questions are addressed. Through this review state-of-the-art knowledge regarding the performance gap of buildings is presented. Major conclusions are drawn and future research directions are pointed out

High-density thermal sensitivity maps of the human body

‘Personal comfort systems’ and thermally active clothing are able to warm and cool individual building occupants by transferring heat directly to and from their body surfaces. Such systems would ideally target local body surfaces with high temperature sensitivities. Such sensitivities have not been quantified in detail before. Here we report local thermal sensations and sensitivities for 318 local skin spots distributed over one side of the body, measured on a large number of subjects. Skin temperature changes were induced with a thermal probe 14 mm in diameter, and subjective thermal sensations were surveyed after 10 s. Our neutral base temperature was 31 °C and the spot stimulus was ±5 °C. Cool and warm sensitivities are seen to vary widely by body part. The foot, lower leg and upper chest are much less sensitive than average; in comparison, the cheek, neck back, and seat area are 2–3 times as sensitive to both cooling and warming stimuli. Every body part exhibits stronger sensitivity to cooling (1.3–1.6 times stronger) than to warming. Inter-personal differences and regional variance within body parts were observed to be 2–3 times greater than potential sex differences. These high-density thermal sensitivity maps with appended dataset provide the most comprehensive distributions of cold and warm sensitivity across the human body

High-density thermal sensitivity maps of the human body

‘Personal comfort systems’ and thermally active clothing are able to warm and cool individual building occupants by transferring heat directly to and from their body surfaces. Such systems would ideally target local body surfaces with high temperature sensitivities. Such sensitivities have not been quantified in detail before. Here we report local thermal sensations and sensitivities for 318 local skin spots distributed over one side of the body, measured on a large number of subjects. Skin temperature changes were induced with a thermal probe 14 mm in diameter, and subjective thermal sensations were surveyed after 10 s. Our neutral base temperature was 31 °C and the spot stimulus was ±5 °C. Cool and warm sensitivities are seen to vary widely by body part. The foot, lower leg and upper chest are much less sensitive than average; in comparison, the cheek, neck back, and seat area are 2–3 times as sensitive to both cooling and warming stimuli. Every body part exhibits stronger sensitivity to cooling (1.3–1.6 times stronger) than to warming. Inter-personal differences and regional variance within body parts were observed to be 2–3 times greater than potential sex differences. These high-density thermal sensitivity maps with appended dataset provide the most comprehensive distributions of cold and warm sensitivity across the human body