Kazem Hussein A, Yousif Jabar H, Chaichan Miqdam T, Al-Waeli Ali H A, Sopian K
Sohar University, PO Box 44, Sohar, PCI 311, Oman.
Solar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
Heliyon. 2022 Jan 21;8(1):e08803. doi: 10.1016/j.heliyon.2022.e08803. eCollection 2022 Jan.
This paper evaluated a 1.4 kW grid-connected photovoltaic system (GCPV) using two neural network models based on experimental data for one year. The novelty of this study is to propose and compare full recurrent neural network (FRNN), and principal component analysis (PCA) models based on entire year experimental data, considering limited research conducted to predict GCPV behaviour using the two methods. The system data was collected for 12 months secondly and hourly data with 50400 samples daily. The GCPV evaluates using specific yield, energy cost, capacity factor, payback period, current, voltage, power, and efficiency. The predicted GCPV current and power using FRNN and PCA were evaluated and compared with measured values to validate results. However, the results indicated that FRNN is better in simulating the experimental results curve compared with PCA. The measured and predicted data are compared and evaluated. It is found that the GCPV is suitable and promising for the study area in terms of technical and economic evaluation with a 3.24-4.82 kWh/kWp-day yield, 21.7% capacity factor, 0.045 USD/kWh cost of energy, and 11.17 years payback period.
本文基于一年的实验数据，使用两种神经网络模型对一个1.4千瓦的并网光伏系统（GCPV）进行了评估。本研究的新颖之处在于，考虑到使用这两种方法预测GCPV行为的研究有限，提出并比较了基于全年实验数据的全递归神经网络（FRNN）和主成分分析（PCA）模型。其次，收集了该系统12个月的数据，每天每小时的数据有50400个样本。通过特定产量、能源成本、容量系数、回收期、电流、电压、功率和效率对GCPV进行评估。使用FRNN和PCA预测的GCPV电流和功率与测量值进行了评估和比较，以验证结果。然而，结果表明，与PCA相比，FRNN在模拟实验结果曲线方面表现更好。对测量数据和预测数据进行了比较和评估。结果发现，从技术和经济评估来看，GCPV对于研究区域是合适且有前景的，其产量为3.24 - 4.82千瓦时/千瓦·日，容量系数为21.7%，能源成本为0.045美元/千瓦时，回收期为11.17年。
