The need to understand the influence of environmental

The gap

Evidence profile

Sourced from the stated research gap and future-work section and limitations of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 3 journals. Those papers have been cited 51 times in total.

Supporting evidence — 3 representative gaps

• Predictive Energy and Exergy Assessment of Photovoltaic Systems Under Dynamic Environmental Conditions Using Machine Learning (2026) · Applied Sciences · doi

  The need to understand the influence of environmental and operational parameters on photovoltaic system performance. The lack of comprehensive evaluations of photovoltaic systems using machine learning techniques. The importance of integrating energy and exergy analyses to evaluate the overall performance of photovoltaic systems.

  generalstated research gap
  Keywords: need understand influence environmental operational parameters photovoltaic system
• Machine learning-based solar radiation prediction across heterogeneous climatic zones using NASA POWER data: a case study in Nigeria (2026) · Frontiers in Artificial Intelligence · doi

  Further research is needed to evaluate the performance of the machine learning models in other locations and datasets. The study could be extended to consider other factors that may affect solar radiation. Other machine learning models could be explored for solar radiation prediction. The use of more advanced techniques, such as deep learning, could be investigated.

  generalfuture-work section
  Keywords: further research needed evaluate performance machine learning models
• Deep regression analysis for enhanced thermal control in photovoltaic energy systems (2024) · Scientific Reports · cited 51× · doi

  • This research focuses on quantifying cooling efficiency, and further research is needed to explore the ap- plication of deep learning for other aspects of PV system performance, such as predicting energy output or identifying potential faults. • The accuracy of the proposed method may be affected by factors such as image quality, lighting conditions, and the presence of obstructions. Further investigation is needed to assess the robustness of the method under different conditions. • The current dataset is limited in size and scope, and further data collection and annotation are required to improve the model’s generalization ability and ensure its applicability to a wider range of PV systems. • The cost of implementing the proposed system needs to be further investigated to determine if it is economi- cally viable for smaller-scale PV installations.

  generallimitationsevidence 5/5
  Keywords: further needed system proposed conditions focuses quantifying cooling efficiency explore plication deep learning aspects performance

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