Hybrid Solar Energy Forecasting with Supervised Deep Learning in IoT Environment
Bibliographic record
Abstract
Microgrids, which integrate decentralized “renewable energy sources (RESs)”, energy storage devices, and load management methodologies, have arisen lately as a building ingredient for smart grids. The intermittent nature of renewable energy sources offers many barriers to smart microgrids, including "reliability, voltage quality, and supply-demand balance". Thus, predicting power output from renewable energy sources "(such as wind turbines and solar panels)" has become more vital for the efficient and continuous functioning of the electrical grid, as well as for attaining optimum RES utilization. Smart microgrids also incorporate energy demand forecasting, which assists in the planning of power production and energy trading with the commercial grid. Models based on “MLand DL” are promising ways of forecasting consumer needs and energy production from “renewable energy sources”. In this context, this research article presents a full review of the available DL-based algorithms for “wind turbine and solar panel power forecasting, as well as electric power demand forecasting”. It also goes through the datasets that were used to train and test the different DL-based prediction models, enabling future researchers to pick the correct datasets for their projects. A secondary way of data collecting is considered for this work to obtain information from various papers and journals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".