LSTM and RBFNN Based Univariate and Multivariate Forecasting of Day-ahead Solar Irradiance for Atlantic Region in Canada and Mediterranean Region in Libya
Bibliographic record
Abstract
For optimal functioning, grid-connected photovoltaic (GCPV) systems need day-ahead power forecasting, as this ensures overall enhanced management in areas such as reliability, scheduling, and efficiency in energy trading. Solar irradiation forecasts are especially important for obtaining photovoltaic (PV) power production predictions, given that that PV output represents a function of solar irradiation. Recently, the Long Short-Term Memory (LSTM) model is being increasingly applied in solar irradiance forecasting, but the performance of LSTM is still relatively unknown. The present paper explores how meteorological and geographical (i.e., exogenous) and past records of solar irradiance (i.e., endogenous) variables may be incorporated as input features in day-ahead solar irradiance forecasting models that use deep learning models. In this study, the results for the LSTM model are compared to those for the Radial Basis Function neural network (RBFNN) in relation to both multivariate time series forecasting (MTSF) and univariate time series forecasting (UTSF). The results of the comparisons show that the UTSF_LSTM model performs better than other models with regard to minimum forecasting errors. Our results have also been validated with data from a region that features different climatic conditions from those originally tested. Overall, the outcome of these investigations clearly indicate the superiority of the proposed UTSF_LSTM method when compared to the UTSF_RBFNN, MTSF_RBFNN, or MTSF_LSTM developed models with regard to the coefficient of determination (R2) and the Root Mean Square Error (RMSE).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".