29 تیر 1403
امين شهسوارگلدانلو

امین شهسوارگلدانلو

مرتبه علمی: استادیار
نشانی: دانشگاه صنعتی کرمانشاه - دانشکده مهندسی - گروه مهندسی مکانیک
تحصیلات: دکترای تخصصی / مهندسی مکانیک
تلفن:
دانشکده: دانشکده مهندسی

مشخصات پژوهش

عنوان
Application of PSO–ANN modelling for predicting the exergetic performance of a building integrated photovoltaic/thermal system
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
Artificial neural network (ANN) · Optimization · Particle swarm optimization (PSO) · BIPV/T · Exergy
پژوهشگران جلال الصراف (نفر اول)، حسین مویدی (نفر دوم)، احمد صفوان الرشید (نفر سوم)، محمد عبدالهی مائوزو (نفر چهارم)، امین شهسوارگلدانلو (نفر پنجم)

چکیده

The main objective of this study is to examine the feasibility of hybrid PSO–ANN technique to estimate the exergetic performance of a building integrated photovoltaic/thermal (BIPV/T) system. A performance evaluation criterion (PEC) is defined in this study to assess the overall performance of a BIPV/T system from exergy point of view. Then, the mentioned method is utilized to identify a relationship between the input and output parameters of the BIPV/T system. The parameter PEC was taken as the essential output of the BIPV/T system, while the input parameters were channel length, channel depth, channel width, and air mass flow rate. Prior to PSO, variables of ANN algorithm were optimized. In addition, PSO influential parameters such as swarm size, personal learning coefficient, global learning coefficient, and inertia weight were optimized using a series of trial-and-error process. The predicted results for data sets from ANN and PSO–ANN models were evaluated according to several known statistical indices and novel ranking systems of color intensity rating and total ranking method. The obtained RMSE and R2 in the training (RMSE of 0.010274 and 0.006112, and R2 of 0.9968 and 0.9989, respectively, for the PSO and ANN methods) and testing (RMSE of 0.011146 and 0.005927, and R2 of 0.9967 and 0.9990, respectively, for the PSO and ANN methods) phases. The results revealed that the PSO–ANN network model could slightly accomplish a better performance when it is compared to the conventional ANN.