Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches
Abstract
:1. Introduction
2. Materials and Methods
2.1. Solar Energy Generation Design for KAU Hospital
2.2. The Solar Power Plant Types
2.3. PV System Design
2.3.1. Determining the Hourly Distribution of the Energy Consumption of the Harran University Hospital
- The maximum energy consumption of HUH is in July.
- Electricity consumption is the highest in 4 months (summer period) from June to September,
- Electricity consumption is the lowest in the period of 6 months (winter period) from November to April,
- The electricity consumption profiles of an average day obtained for May and October are similar to the consumption profile obtained for an average day determined according to annual data.
2.3.2. Determining the Load Profile of the Energy Consumption of the Harran University Hospital
2.3.3. The Hourly Energy Consumption Distribution of the KAU Hospital
3. Solar PV System Analysis and Performance Prediction
3.1. Data Collection and Analysis
3.2. RSM for Optimization of Solar PV System
3.3. ANFIS Approach for PV Efficiency Estimation and Analysis
4. Results and Discussions for Solar PV System Findings
4.1. The Assessment of PV System Simulation
The Assessment of Solar PV Module Using RSM Approach
4.2. The Assessment of Performance of Developed Models Using ANFIS Approach
4.3. Comparison of the Results with the Cases Introduced by Other Works
5. Conclusions
- A solar PV system with a capacity of 35 MW and/or more will be sufficient for the KAU hospital and meet the electrical energy demand of the hospital;
- For a PV system of 40 MW capacity, the maximum electricity generation is approximately between 26 and 31 MWh. Hence, the hourly maximum electrical energy requirement of the hospital between 11:00 and 15:00 h can be met by the PV system during all months;
- In all PV designs and simulation tests, the highest electrical energy production during the year was observed in March;
- The self-sufficiency ratio for March was 31% for PV25, 37% for PV30, 43% for PV35, 50% for PV40, 56% for PV45, 62% for PV50, 93% for PV75 and 124% for PV100;
- The self-sufficiency ratio for the yearly period was found as 24% for PV25, 29% for PV30, 34% for PV35, 39% for PV40, 44% for PV45, 48% for PV50, 73% for PV75 and 97% for PV100.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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PV25 | PV30 | PV35 | PV40 | PV45 | PV50 | PV75 | PV100 | |
---|---|---|---|---|---|---|---|---|
January | 0.22 | 0.27 | 0.31 | 0.36 | 0.40 | 0.45 | 0.67 | 0.90 |
February | 0.19 | 0.23 | 0.27 | 0.31 | 0.35 | 0.39 | 0.58 | 0.78 |
March | 0.26 | 0.31 | 0.36 | 0.41 | 0.47 | 0.52 | 0.78 | 1.03 |
April | 0.27 | 0.32 | 0.37 | 0.42 | 0.48 | 0.53 | 0.80 | 1.06 |
May | 0.31 | 0.37 | 0.43 | 0.50 | 0.56 | 0.62 | 0.93 | 1.24 |
June | 0.24 | 0.29 | 0.33 | 0.38 | 0.43 | 0.48 | 0.72 | 0.96 |
July | 0.25 | 0.29 | 0.34 | 0.39 | 0.44 | 0.49 | 0.74 | 0.98 |
August | 0.26 | 0.31 | 0.36 | 0.41 | 0.46 | 0.51 | 0.77 | 1.02 |
September | 0.25 | 0.30 | 0.36 | 0.41 | 0.46 | 0.51 | 0.76 | 1.01 |
October | 0.23 | 0.28 | 0.33 | 0.37 | 0.42 | 0.46 | 0.70 | 0.93 |
November | 0.22 | 0.27 | 0.31 | 0.36 | 0.40 | 0.45 | 0.67 | 0.90 |
December | 0.23 | 0.27 | 0.32 | 0.36 | 0.41 | 0.45 | 0.68 | 0.90 |
Yearly | 0.24 | 0.29 | 0.34 | 0.39 | 0.44 | 0.48 | 0.73 | 0.97 |
Radiation-(W/m2) | Module Surface Temperature-(°C) | Outdoor Temperature-(°C) | Wind Direction | Wind Speed-(m/s) | Actual PV (MW) | Predicted PV by ANFIS (MW) | Predicted PV by RSM (MW) |
---|---|---|---|---|---|---|---|
896.33 | 43.73 | 26.16 | 232.64 | 3.63 | 19.98 | 19.98 | 19.99 |
826.38 | 41.64 | 25.05 | 235.65 | 3.33 | 21.01 | 22.00 | 21.22 |
658.68 | 37.27 | 28.08 | 59.63 | 3.02 | 19.93 | 19.95 | 19.94 |
589.66 | 49.96 | 39.08 | 262.91 | 3.35 | 24.95 | 24.96 | 24.93 |
573.58 | 48.56 | 38.70 | 278.39 | 4.42 | 25.64 | 25.64 | 25.65 |
570.30 | 46.60 | 36.26 | 218.72 | 3.95 | 25.81 | 25.72 | 25.59 |
561.30 | 41.62 | 30.26 | 125.83 | 1.28 | 24.64 | 24.65 | 24.91 |
552.27 | 46.25 | 36.21 | 216.56 | 3.97 | 25.03 | 25.05 | 25.29 |
548.65 | 38.49 | 32.64 | 63.35 | 3.15 | 26.20 | 26.21 | 26.33 |
538.25 | 46.39 | 37.40 | 89.96 | 2.52 | 24.23 | 24.22 | 24.13 |
533.89 | 47.18 | 36.54 | 214.12 | 3.25 | 23.76 | 23.79 | 23.51 |
530.75 | 37.48 | 33.72 | 182.00 | 5.90 | 25.26 | 25.28 | 25.37 |
526.28 | 35.71 | 30.55 | 39.37 | 3.73 | 25.41 | 25.45 | 25.31 |
520.78 | 47.59 | 38.69 | 285.97 | 4.44 | 23.36 | 23.37 | 23.42 |
PV Model Power Output (Aldair et al.) [37] | Our PV Model Power Output | |||
---|---|---|---|---|
Radiation | Temperature (°C) | ANFIS (MW) | ANFIS (MW) | RSM (MW) |
500 | 0 | 33.36 | 46.14 | 48.50 |
500 | 25 | 27.72 | 34.78 | 35.23 |
500 | 50 | 22.58 | 32.02 | 30.61 |
750 | 0 | 51.4 | 56..12 | 52.98 |
750 | 25 | 43.6 | 47.78 | 41.19 |
750 | 50 | 35.98 | 36.86 | 34.53 |
1000 | 0 | 69.4 | 70.25 | 71.36 |
1000 | 25 | 59.1 | 58.17 | 60.88 |
1000 | 50 | 48.74 | 43.79 | 39.24 |
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Alamoudi, R.; Taylan, O.; Aktacir, M.A.; Herrera-Viedma, E. Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches. Mathematics 2021, 9, 2929. https://doi.org/10.3390/math9222929
Alamoudi R, Taylan O, Aktacir MA, Herrera-Viedma E. Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches. Mathematics. 2021; 9(22):2929. https://doi.org/10.3390/math9222929
Chicago/Turabian StyleAlamoudi, Rami, Osman Taylan, Mehmet Azmi Aktacir, and Enrique Herrera-Viedma. 2021. "Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches" Mathematics 9, no. 22: 2929. https://doi.org/10.3390/math9222929