A novel snow conditions‐compatible computational intelligence‐based PV power forecasting approach for microgrids in snow prone regions
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Bibliographic record
Abstract
Abstract Energy management in a renewable energy‐based microgrid has a key role in improving energy utilisation and reducing the microgrid operation cost. The optimal energy management strategy can be significantly affected by the intermittency of renewable energies and also harsh weather conditions. In this study, a novel snow conditions‐compatible computational intelligence‐based short‐term photovoltaic (PV) power forecasting (PVPF) approach is proposed that is independent of exogenous weather forecasts. The proposed approach consists of a snow cover detection stage, a snow cover forecasting stage, and a PV power forecasting stage. This approach is then validated for a model predictive control (MPC)‐based energy management system (EMS) of a PV energy‐based grid‐connected microgrid located in a snow‐prone area. The PVPF method together with a computational intelligence‐based short‐term load demand forecasting model constitutes the forecasting block of the EMS. The forecasting block generates day‐ahead hourly forecasts based on the local measurements of the meteorological‐electrical parameters and sends them to the optimisation block where a two‐stage control method, corresponding to the tertiary and secondary control levels, is developed based on mixed‐integer linear and quadratic programming. The developed EMS is applied to a test microgrid simulated in MATLAB/Simulink and compared with a heuristic control method. The results show that the proposed approach can reduce the overall operation cost of the microgrid by 8% (24$), 15% (166$), and 13% (235$) on sunny, cloudy, and snowy days under study, respectively, compared to the heuristic controller.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it