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Application of PSO–ANN modelling for predicting the exergetic performance of a building integrated photovoltaic/thermal system

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Abstract

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.

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Abbreviations

\({C_{\text{P}}}\) :

Specific heat capacity of air (J kg− 1 K− 1)

\({D_{\text{H}}}\) :

Hydraulic diameter of PV duct located behind the PV modules (m)

\({h_{\text{c}}}\) :

Convective heat exchange coefficient (W K− 1 m− 2)

\({h_{{\text{r}},{\text{pv}} - {\text{b}}}}\) :

Radiative heat exchange coefficient from PV modules to insulation wall (W K− 1 m− 2)

\({h_{{\text{r}},{\text{pv}} - {\text{s}}}}\) :

Radiative heat exchange coefficient between PV modules and sky (W K− 1m− 2)

\({h_{\text{w}}}\) :

Heat transfer coefficient of wind (W Km− 2)

\({I_{\text{r}}}\) :

Intensity of solar radiation (W m− 2)

K :

Thermal conductivity of air (W m− 1 K−)

\({k_{{\text{ins}}}}\) :

Thermal conductivity of insulation material (W m− 1 K− 1)

\(L\) :

Duct length (m)

\({\dot {m}_{\text{f}}}\) :

Mass flow rate of air (kg s− 1)

PEC:

Performance evaluation criterion

\(\Delta p\) :

Pressure loss (Pa)

Pr:

Prandtl number

Re:

Reynolds number

S :

Duct depth (m)

Ta:

Temperature of outdoor air (K)

Tb:

Temperature of insulation wall (K)

Tf:

Temperature of flowing air in the duct (K)

\({T_{{\text{in}}}}\) :

Temperature of air entering the duct (K)

\({T_{{\text{mf}}}}\) :

Mean air temperature (K)

Tpv:

PV module temperature (K)

Ub:

Back-loss coefficient (W K− 1m− 2)

vw:

Wind velocity (m s− 1)

W :

Duct width (m)

\({\dot {X}_{{\text{air}},{\text{in}}}}\) :

Exergy of air entering the duct (kWh)

\({\dot {X}_{{\text{air}},{\text{out}}}}\) :

Exergy of air leaving the duct (kWh)

\({\dot {X}_{{\text{dest}}}}\) :

Exergy destructed from the collector (kWh)

\({\dot {X}_{{\text{el}},{\text{PV}}}}\) :

Exergy of electricity produced by PV modules (kWh)

\({\dot {X}_{{\text{fan}}}}\) :

Exergy of electricity consumed by fans (kWh)

\({\dot {X}_{\text{r}}}\) :

Exergy load of the external air (kWh): solar radiation exergy (kWh)

\({\dot {X}_{{\text{th}}}}\) :

Thermal exergy output (kWh)

\({\alpha _{{\text{pv}}}}\) :

Sorptance of PV modules

\({\delta _{{\text{ins}}}}\) :

Thickness of insulation material (m)

\({\varepsilon _{{\text{pv}}}}\) :

Emissivity of PV module

\({\eta _{{\text{el}}}}\) :

Electrical conversion efficiency of PV modules

\({\eta _{{\text{fan}}}}\) :

Fan efficiency

\(\rho\) :

Air density (kg m− 3)

\(\sigma\) :

Stefan–Boltzmann constant (5.67 × 10− 8 W m− 2 K− 4)

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Author information

Authors and Affiliations

  1. Public Authority of Applied Education and Training, College of Technological Studies (CTS), Automotive and Marine Engineering Department, 70654, Shuwaikh, Kuwait

    Jalal Alsarraf

  2. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  3. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  4. Department of Geotechnics and Transportation, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Ahmad Safuan A. Rashid

  5. Civil Engineering Department, University of Hafr Al-Batin, Al-Jamiah, Hafr Al-Batin, 39524, Eastern Province, Kingdom of Saudi Arabia

    Mohammed Abdullahi Muazu

  6. Department of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, Iran

    Amin Shahsavar

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  1. Jalal Alsarraf
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Correspondence to Hossein Moayedi.

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Alsarraf, J., Moayedi, H., Rashid, A.S.A. et al. Application of PSO–ANN modelling for predicting the exergetic performance of a building integrated photovoltaic/thermal system. Engineering with Computers 36, 633–646 (2020). https://doi.org/10.1007/s00366-019-00721-4

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