本研究為太陽光電熱能複合系統(PV/T)之最佳化參數設計，主要探討PV/T設計參數與品質特性之關係，PV/T的兩個品質特性分別為發電效率與儲熱效率，而設計及製造PV/T時，影響PV/T的重要參數為集熱板材質、收集器傾斜角、收集器方位角、集熱管數目、質量流率、儲水槽容積/收集器面積(V/A)比等六種，本研究首先利用性能模擬測試軟體預測最佳化PV/T系統參數，透過田口方法(Taguchi Method)規劃實驗，並搭配主效果分析與變異數分析(Analysis of Variance, ANOVA)理論，對發電效率與儲熱效率兩個品質特性實驗結果分別進行單一品質特性分析，然後將各品質數據進行灰關聯生成之數據前處理，再配合熵測度(Entropy)進行灰關聯分析(Grey Relational Theory)，另一方面，再利用層級分析法(Analytic Hierarchy Process, AHP)的層級架構得到正對比矩陣，經由一致性的檢查與計算各參數水準的總權重，並排序其重要性得到最佳化參數因子及水準。經過上述灰關聯分析與層級分析作相互地驗證後，找出多重品質最佳化之因子與水準組合，再經確認實驗，發電效率與儲熱效率的訊號雜訊比皆落於95%信賴區間，顯示本實驗具有再現性且值得信賴，最佳參數化組合為：集熱板材質為銅、收集器方位角為正南方、集熱管數目為12支、質量流率為0.01kg/s-m2、收集器傾斜角為25°、V/A比為123。
研究結果顯示性能模擬測試實驗數據總效率最大值可達64.97%、最小值為44.38%，實體性能測試總效率最大值可達64.45%、最小值為41.36%，經由性能效率曲線評估，性能模擬測試與實體性能測試之儲熱效率係數與發電效率係數誤差率分別為2.78%和2.16%，顯示性能模擬測試具有良好的預測效果。
另外，性能模擬測試與實體性能測試總效率之相關係數(Correlation Coefficient)和方均根百分比偏差(Root Mean Square Percent Deviation)分別為0.99和3.47%，相關係數非常接近1，顯示本研究的實體性能測試具有良好的應證效果。

This study aimed to optimize the parameter design for solar photovoltaic and thermal composite system (PV/T), and discuss the relationship between the PV/T design parameters and quality characteristics. The two PV/T quality characteristics were power generation efficiency and heat storage efficiency. In the design and manufacturing of PV/T, major influencing factors included solar panel material, collector azimuth angle, collector inclination angle, tube number, the mass flow rate, storage tank volume/collector area (V/A) ratio. Therefore, this study first used the performance simulation test software to predict the optimized PV/T system parameters. The Taguchi method was applied in experiment planning, coupled with the principle effect analysis and the analysis of variance theory, to conduct the single quality characteristic analysis of the experimental results of two quality characteristics including power generation efficiency and heat storage efficiency. Next, this study preprocessed the grey relational data generated from various quality data before the grey relational analysis by using entropy. The Analytic Hierarchy Process (AHP) was employed to obtain the positive comparative matrix. After the consistency checks and the calculation of the total weights of the various parameter levels, the ranking of the parameters was determined to obtain the optimized parameter factors and their levels. After the mutual confirmation by using the grey relational theory and AHP as described above, this study determined the multiple quality optimization factor and level combinations. According to the confirmation experiment, the S/N ratios of power generation efficiency and heat storage efficiency fell within the 95% confidence interval, suggesting that the experiment can be reproducible and trustworthy. The optimal parameter combination is: copper solar panel, collector inclination angle at 25°, 12 tubes, the mass flow rate at 0.01kg/s-m2, collector azimuth angle at the south, V/A ratio at 123.
The results suggested that the maximum value of the overall efficiency of the performance simulation test experimental data can reach 64.97%, and the minimum value is 44.38%. The maximum value of the physical performance test overall efficiency is 64.45%, and the minimum value is 41.36%. The performance efficiency curve assessment found that the error rates for heat storage efficiency coefficient and the power generation efficiency coefficient in the performance simulation test and the physical performance test are 2.78% and 2.16%, suggesting that the performance simulation test has good prediction effects.
The deviations of the correlation coefficient and root mean square percentage of the total efficiency of performance simulation test and the physical performance test are 0.99 and 3.47% respectively. The correlation coefficient is very close to 1, suggesting that the physical performance test has good confirmation effects.

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