Dear Authors,

I read your manuscript with pleasure and find it interesting. I will provide my initial set of responses in the following text.

Summary: The manuscript argues that better forecasting of solar PV generation at a higher temporal resolution is necessary for solar PV infrastructure analysis. This becomes even more important because large overestimation of solar PV generation is observed in the  authoritative datasets. The authors propose to correct for this bias by introducing a Physics informed itransformer model. The authors also take us through the full forecast pipeline all the way from GNI forecasting to comparative PV power forecasting results. The authors claim that their PINN-itransformer model provides higher fidelity and better accuracy in very short term PV power forecasting, but may still lack in relatively long term forecasting tasks. 

Reviewer comments: 

While the manuscript is surprisingly well written and concise, I do have some general comments to help communicate the manuscript better.

1.) In data and Methods:

• A) Why "The initial 12 hours of each simulation run were discarded". Add more context here on why only 12 and what happens when you discard this initial forecast?
• B) Clarity is required on what is meant by - itransformer, physics-itransformer, PC-itransformer, PINN-itransformer. These terminologies are used further in the results section. What I find is that the writeup for these models in data and section seems to look OK, but it is confusing due to cross referencing with previous published work. I suggest that more details and fundamental information be added in the 4 models to make it accessible for readers. 
• C) G_POA is used here, but defined only in the results section. I suggest rewriting the description of models from scratch, do not assume that the readers would have read the referenced work by Fan, Sun et al. etc. before. I would suggest that the authors follow this pattern
  • § Describe the model in plain English by adding what the model can do and give an application example
  • § Describe the core functioning of the model, its equations and layer description
  • § Describe in detail how this model will be used in the subsequent steps/validation studies.

eg. 𝒅𝑷(𝒕)/𝒅𝒕 = −𝒌·(𝑷̂(𝒕)−𝑷𝒆𝒒(𝒕)) this equation just comes from nowhere and we do not get a context on why this is important. The only pointer is that some components eg. equilibrium is defined from Fan et al. paper

2) Data Engineering

• A) GHI correction: the 400/100/400 split is counterintuitive, Here you are loosing nearly 50% of your data points. I would suggest using stratified sampling and doing 10 fold cross validation technique to use all data for training and prediction. e.g. in the 10 fold cross validation 90% of the samples are used for training and 10% for validation, this is then used in conjunction with rolling window approach to use all data for training. If this is unsuitable then do 700/100/100 split. 
• B) Why use only RMSE/MAE as the metric for evaluation, why not other error metics. What I mean is that define the rational for RMSE/MAE. 
• C) Why is this logical? "the inherent challenge for neural networks to model long-range temporal dependencies". what are the inherent challenges? why not use RNN/LSTM for long term forecast? How long is the long term forecast? I am assuming here that the forecast cycle can have seasonal and repetitive behaviour after 365 days?
• D) Please define in detail what the residuals are in different models. I assume that in the physics-itransformer, the physics model is predicting the PV output and you compare that with actual observations to generate the residuals?

3) For figure number 15 comparisons, I think it would be beneficial if the authors can provide the ranking of the different comparative models to understand how accurate the PINN-itransformer is from the current state of art. 

4) Code-> I went through the Zenodo repository, here my recommendation would be to revoke hardcoded file paths from the python files. Additionally, please add some user manual here e.g. steps/order in which the files would be run. 

5) Limitations -> Add limitations of the model eg. this model is only training on the historical data or very short term real data. Would this model break if we do the forecasting for 1 year/5 year/20 years? What is the result of validation with SolarGIS data at monthly level? What will happen when we forecast for regions that are in gobi desert etc. where there is not much cloud based variability. Have you tested for 2025 time series as the model was trained for 2020 timer series. Add additional limitations that the authors seems necessary

I hope that the suggestions will help you in refining an already b=nice manuscript. All the best with the updates.

Reviewer Comments

This manuscript presents a multi-scale photovoltaic (PV) power forecasting framework (MIPV-NWP-PINN) integrating WRF-based numerical weather prediction, a hybrid GHI bias correction model (QM-TPA-LSTM), and a physics-informed deep learning architecture (PINN-iTransformer). The topic is timely and relevant, and the proposed framework is technically sound. The combination of NWP, statistical correction, and PINN-based forecasting represents a novel and valuable contribution to PV power prediction. Nevertheless, several issues related to clarity, methodological justification, generalizability, and interpretability should be addressed.

Major Comments

1. In the abstract, the statement “raw Numerical Weather Prediction (NWP) outputs often fail to provide reliable forecasts because PV power is influenced by multiple coupled factors, including meteorological factors and photovoltaic modules” may be misleading. As currently written in the abstract, it may give the impression that PV power is directly predicted from NWP outputs. The authors are encouraged to clarify that NWP provides meteorological inputs, and that uncertainties arise from both atmospheric forecast errors and the subsequent PV power conversion process.
2. The literature review presented in the Introduction is relatively lengthy and could be streamlined. In Section 1, it is suggested to reorganize this part around the core research problems by clearly summarizing: (1) the limited accuracy of NWP-derived GHI; (2) the “black-box” nature and lack of physical consistency in many PV power forecasting models; and (3) the potential of Physics-Informed Neural Networks (PINNs) to embed physical laws into the loss function and improve physical consistency. A more concise, problem-oriented review would improve readability and strengthen the overall motivation.
3. While multi-site validation is conducted for GHI correction, the PV power forecasting results presented in Section 4 (e.g., the multi-horizon comparisons in Figures 12–15) are mainly based on a single station. The applicability of the proposed PINN-iTransformer to other regions, climatic conditions, and PV configurations should therefore be discussed more explicitly, or additional validation should be provided.
4. In the description of the PINN-iTransformer framework, particularly in the subsection introducing the physics-informed loss and the first-order relaxation ODE, the physical meaning of the relaxation coefficient k is not sufficiently explained. In addition, the role of the physics-informed loss weight (λ) is introduced without further discussion. Clarifying whether k is constant or learnable, and providing a brief sensitivity or ablation analysis for λ, would strengthen the physical interpretability and robustness of the proposed approach.

Minor Comments

1. Although the manuscript emphasizes the importance of aerosol and cloud effects on GHI in the Introduction, the QM-TPA-LSTM model described in the GHI correction section remains purely statistical. This limitation should be more clearly acknowledged and discussed, particularly in relation to physical interpretability.
2. While improved PV power forecasting accuracy is demonstrated in the results section, the manuscript does not discuss how these forecasts could be used in downstream applications such as grid operation or energy management. A brief discussion in the concluding section would enhance the applied value of the study.
3. In the PV power forecasting analysis, additional investigation of prediction errors—such as SHAP analysis or other interpretability methods—would be beneficial for understanding the sources of model error and the relative importance of input variables.
4. Throughout the manuscript, please ensure consistent terminology, particularly for forecasting horizons (e.g., “6 h” vs. “6-hour” vs. “6 hours”) and model names (e.g., PINN-iTransformer vs. PINN–iTransformer).
5. In the methodological sections and equations, several symbols (e.g., k, λ, and variables related to PV power and irradiance) are introduced without being clearly defined at first occurrence. Providing clearer definitions or a concise summary of symbols would improve readability.
6. In the multi-panel figures presenting PV forecasting results, some fonts and legends are relatively small. Improving font size and legend placement would enhance clarity.
7. Please ensure that physical units (e.g., W m⁻², kW, MW) are consistently formatted throughout the manuscript and that spacing between numbers and units follows journal conventions.