Predicting Photovoltaic Power Output with Convolutional Neural Networks: A Case Study in Cepu, Central Java, Indonesia
Bibliographic record
Abstract
The prediction of the general national electricity plan has stated that Indonesia's electricity needs in 2038 will increase to 1,000 TWh or the equivalent of 3.3 MWh per capita.If this trend continues, then by 2050, per capita energy consumption is expected to reach 7.7 MWh or 2,600 TWh.This prediction is important because it is to prepare power plant infrastructure throughout the region for the coming period.By applying and adding learning algorithms based on Convolutional Neural Networks (CNN) to developing solar power plants, CNN-based Forecast can increase accuracy by up to 30% and has the ability to train models up to 2 times faster than currently available algorithms.This new algorithm can more accurately detect leading demand indicators, such as pre-order information, increased product demand, price changes, and promotional spikes, to build more accurate forecasts.That before the discovery of CNN there was no algorithm that provided the most accurate estimates for all types of data.Traditional statistical models have been useful in predicting the demand for products that have regular demand patterns, such as summer or winter electricity production.However, statistical models cannot provide accurate forecasts for more complex scenarios, such as frequent changes in energy prices, differences between regional versus national demand, products with different selling speeds, and additions of new products.As for CNN, it is appropriate to predict the power from the panel, with a MAPE performance value of 18.7633%, MAE of 0.0176 and RMSE of 0.0466 so that the prediction system built has fulfilled the prediction of the targeted system.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".