A Review of Machine Learning-Based Photovoltaic Output Power Forecasting: Nordic Context
Bibliographic record
Abstract
Motivated by factors such as the reduction in cost and the need for a shift towards achieving UN’s Sustainable Development Goals, PV (Photovoltaic) power generation is getting more attention in the cold regions of the Nordic countries and Canada. The cold climate and the albedo effect of snow in these regions present favorable operating conditions for PV cells and an opportunity to realize the seasonal matching of generation and consumption respectively. However, the erratic nature of PV brings a threat to the operation of the grid. PV power forecasting has been used as an economical solution to minimize and even overcome this limitation. This paper is therefore a comprehensive review of machine learning-based PV output power forecasting models in the literature in the context of Nordic climate. The impact of meteorological parameters and the soiling effect due to snow, which is unique to this climate, on the performance of a prediction model is discussed. PV power forecasting models in the literature are systematically classified into multiple groups and each group is analyzed and important suggestions are made for choosing a better model for these regions. Ensemble methods, optimization algorithms, time-series decomposition, and weather clustering are identified as important techniques that can be used to enhance performance. And notably, this work proposed two conceptual approaches that can be used to incorporate the effect of snow on PV power forecasting. Future research needs to focus on this area, which is crucial for the development of PV in these regions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".