6 Conclusions and Recommendations
6.4 Future recommendations
In our model, we used a Monte Carlo optimization method due to its simplicity and low calculation cost. In the future, other optimization methods such as evolutionary algorithms or particle swarm optimization (PSO) could be applied in order to improve the search efficiency and thus reduce the computational time requirements. In our optimization, the running time for the static model with 100,000 iterations is around 30 minutes, and it is about one hour for the dynamic model with 2,000 iterations (as each iteration includes 90
angles). Other optimization methods should be able to decrease this running time, as they do not search all space evenly, which is worthwhile to investigate.
The methodology could additionally be further validated by comparing the outputs with data from experimental setups of the system in the laboratories and verifying the performance of the system in different locations. All of our results are specifically for the location of our case study, and optimization results will be different in other locations. In addition, multi-reflection of surfaces can be investigated by detailed modeling or experiments and results can be compared.
Also, this study can be further improved by investigating the non-homogenous tilt angle and spacing in the BIPV shading system. In that case, optimization of objectives such as power production, heating and cooling loads, and lighting condition would result in different configurations which can be compared to our results.
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Appendices
Appendix A: Daylighting simulation results.
Figure A-1: The (a) spatial daylight autonomy and (b) annual sunlight exposure of the Cornerstone Architecture office in the first scenario (no shades).
Figure A-2: The (a) spatial daylight autonomy and (b) annual sunlight exposure of the Cornerstone Architecture office in the second scenario (all-horizontal shades).
Figure A-3: The (a) spatial daylight autonomy and (b) annual sunlight exposure of the Cornerstone Architecture office in the third scenario (horizontal-vertical shades).
Appendix B: Optimization data of the simple case in the static model. Simple case East and
south (horizontal) South (horizontal) South (Vertical) East (horizontal) East (vertical) Based on maximizing overall value function (𝑂𝑉𝐹) 𝑂𝑉𝐹($) 126.8025 106.4468 89.4472 98.8563 82.9628 Angle (°) 85 90 89 90 89 Number 4 4 4 4 4 Length (cm) 0.4270 0.4270 0.4260 0.4270 0.4230 Based on maximizing power value function (𝑃𝑉𝐹) 𝑃𝑉𝐹(𝑘𝑊) 208.2114 124.8491 121.1089 88.8740 121.2058 Angle (°) 59 59 59 59 59 Number 4 4 4 4 4 Length (cm) 0.4100 0.4270 0.4280 0.4280 0.4680 Based on maximizing power +heat value function (𝑃𝑉𝐹 + 𝐻𝑉𝐹) 𝑃𝑉𝐹 + 𝐻𝑉𝐹 289.0018 181.7084 196.3325 118.5399 162.0037 Angle (°) 90 90 72 90 70 Number 2 1 4 3 4 Length (cm) 0.4930 0.5000 0.4280 0.4980 0.4280 Based on maximizing light value function (𝐿𝑉𝐹) 𝐿𝑉𝐹(𝑘𝑊) 616.5104 571.4536 418.4056 577.8099 422.0948 Angle (°) 81 90 90 90 90 Number 4 4 5 4 5 Length (cm) 0.4030 0.4270 0.3330 0.4280 0.3270
Appendix C: Optimization data of the Cornerstone Architecture office in the static model.
Static model East and south (horizontal) South (horizontal) East (horizontal) East (vertical) Based on maximizing overall value function (𝑂𝑉𝐹) 𝑂𝑉𝐹($) 2.6616e+03 1.9086e+03 665.2938 654.9519 Angle (°) 90 90 90 80 Number 5 3 6 12 Length (cm) 0.4980 0.4980 0.4330 0.4690 Based on maximizing power value function (𝑃𝑉𝐹)
𝑃𝑉𝐹(𝑘𝑊) 6.1330e+03 4.7881e+03 1.3542e+03 1.9578e+03
Angle (°) 58 56 65 63 Number 6 5 6 12 Length (cm) 0.4360 0.4990 0.4360 0.4690 Based on maximizing power +heat value function (𝑃𝑉𝐹 + 𝐻𝑉𝐹)
𝑃𝑉𝐹 + 𝐻𝑉𝐹 1.1458e+04 9.6134e+03 2.2380e+03 2.7733e+03
Angle (°) 90 90 90 80 Number 3 2 5 12 Length (cm) 0.4990 0.4920 0.5 0.4690 Based on maximizing light value function (𝐿𝑉𝐹)
𝐿𝑉𝐹(𝑘𝑊) 7.9924e+03 4.7796e+03 2.6080e+03 2.2006e+03
Angle (°) 90 90 84 1
Number 6 5 6 59
Appendix D: Optimization data based on overall value function of the Cornerstone Architecture office in the dynamic model.
Dynamic model (based on maximizing 𝑂𝑉𝐹)
East and south (horizontal) South (horizontal) East (horizontal) East (vertical) Fully dynamic - changing angle every hour 𝑂𝑉𝐹($) 2.8174e+03 1.9823e+03 766.2633 857.3120 Angle (°) - - - - Number 3 3 5 12 Length (m) 0.5 0.5 0.499 0.4680 Changing angle every season (winter-spring- summer-fall) 𝑂𝑉𝐹($) 2.7499e+03 1.9499e+03 738.6632 781.9217 Angle (°) 90–90–76–90 90–90–41–90 90–90–65–90 90–90–23–90 Number 5 2 5 12 Length (m) 0.5 0.4990 0.4990 0.4680 Changing angle twice a year (every 6 month, Jan 1 – July 1) 𝑂𝑉𝐹($) 2.6621e+03 1.9090e+03 686.7418 696.0313 Angle (°) 90–90 90–90 90–75 90–68 Number 5 3 5 12 Length (m) 0.5 0.5 0.4990 0.4680 Changing angle twice a year (Jan 1 – optimum day) 𝑂𝑉𝐹($) 2.6621e+03 1.9090e+03 697.3538 719.4882 Angle (°) 90–90 90–90 90–73 90–67 Number 5 3 5 12 Length (m) 0.5 0.5 0.4990 0.4680
Appendix E: Optimization data based on power + heat value functions of the Cornerstone Architecture office in the dynamic model.
Dynamic model (based on maximizing
𝑃𝑉𝐹 + 𝐻𝑉𝐹)
East and south (horizontal) South (horizontal) East (horizontal) East (vertical) Fully dynamic - changing angle every hour
𝑂𝑉𝐹($) 1.4467e+04 1.1434e+04 3.1841e+03 4.2895e+03
Angle (°) - - - - Number 3 3 5 12 Length (m) 0.5 0.5 0.499 0.4680 Changing angle every season (winter-spring- summer-fall) 𝑃𝑉𝐹 + 𝐻𝑉𝐹(𝑘𝑊)
1.3979e+04 1.1127e+04 3.0238e+03 3.9206e+03
Angle (°) 90–90–8–90 90–90–8–90 90–90–54–90 90–90–31–90 Number 3 3 5 12 Length (m) 0.5 0.5 0.4990 0.4680 Changing angle twice a year (every 6 month, Jan 1 – July 1) 𝑃𝑉𝐹 + 𝐻𝑉𝐹(𝑘𝑊)
1.2061e+04 9.6260e+03 2.5717e+03 3.4446e+03
Angle (°) 90–54 90–83 90–54 90–54
Number 5 2 5 12
Appendix F: Optimization data based on power value function of the Cornerstone Architecture office in the dynamic model.
Dynamic model (based on maximizing 𝑃𝑉𝐹)
East and south (horizontal) South (horizontal) East (horizontal) East (vertical) Fully dynamic - changing angle every hour
𝑃𝑉𝐹(𝑘𝑊) 6.5310e+03 5.2098e+03 1.4809e+03 2.0872e+03
Angle (°) - - - - Number 6 5 6 12 Length (m) 0.4360 0.4980 0.4320 0.4680 Changing angle every season (winter-spring- summer-fall)
𝑃𝑉𝐹(𝑘𝑊) 6.2841e+03 5.0379e+03 1.3731e+03 2.0011e+03 Angle (°) 57–81–90–57 57–90–90–57 76–57–57–86 70–55–55–78 Number 6 6 6 12 Length (m) 0.4360 0.4360 0.4320 0.4680 Changing angle twice a year (every 6 month, Jan 1 – July 1)
𝑃𝑉𝐹(𝑘𝑊) 6.1330e+03 4.7879e+03 1.3468e+03 1.9607e+03
Angle (°) 57–57 55–55 64–70 59–67
Number 6 5 6 12
Curriculum Vitae
Name: Seyedsoroush Sadatifar
Post-secondary Ferdowsi University of Mashhad
Education and Mashhad, Iran
Degrees: 2012-2016 BESc.
Ferdowsi University of Mashhad Mashhad, Iran
2016-2018 MESc. (Transferred to University of Western Ontario) The University of Western Ontario
London, Ontario, Canada 2019-present MESc.
Honors and Awards:
Related Work Teaching Assistant
Experience University of Western Ontario
2019-2020
MITACS accelerate program
Cornerstone Architecture Company, London, Ontario Jan 2020 – June 2020
Proctor
King’s college, London, Ontario Fall 2019
Teaching assistant
Ferdowsi University of Mashhad, Mashhad, Iran 2016-2018
Publications:
S. Sadatifar, M.M Ghafurian, H. Niazmand. “Study the effects of Environmental condition on the power unit of the CCHP systems”, ISME 2018 Conference, Semnan, Iran
S. Sadatifar, M.M Ghafurian, H. Niazmand. “ Optimization of CCHP System, Investigation of its Pollution and Economic Costs in Ideal and Real conditions in terms of Local and Global Approach”, Journal of Solid and Fluid Mechanics, 2019,