An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S
Bibliographic record
Abstract
Our World gives several symptoms of climate change. Devastating draughts increase (negative for World (-)), global mean temperature increase (-), lightning strikes increase (-), sea ice cover melt (-), tree mortality increase (-), and forest degradation increase (-) have been observed for decades. They are all negative measures for continuity of life. Diversity of species has been decreasing, so that life on Earth is dying. Only responsible specie for this situation is humankind. This study presents a small footstep to prevent this situation. Modeling of a 100% renewable power grid on World (Global Grid) is eminent. Annual peak power load (Gigawatt: GW, Kilowatt: kW) (peak demand or load) forecasting in power demand side is crucial for global grid modeling. This study presents an experimental fuzzy inference system for the third core module (100 years’ power demand forecasting) of the first console (long term prediction) of Global Grid Peak Power Prediction System (G2P3S). The inputs (world population, global annual temperature anomalies °C) and the output (annual peak power load demand of Global Grid in GW) are modeled with seven triangular fuzzy input membership functions and seven constant output membership functions. The constant Sugeno-Type fuzzy inference system is used in the current experimental model. The maximum absolute percentage error (MAP) is calculated as 45%, and the mean absolute percentage error (MAPE) is found as 39% in this experimental study. The MAP and MAPE of the first core module model (Type 1) were 0,46 and 0,36. The MAP and MAPE of the second core module model (Interval Type 2) were 0,46 and 0,36. As a result, this study is a good start for the third core module of the first console on Global Grid Peak Power Prediction System research, development, demonstration, & deployment (RD3) project. This experimental study also warns humankind in this subject. Hopefully, the most polluting societies on our World such as China, United States, India, Russia, Japan, Germany, South Korea, and Canada take urgent actions to start to build the foundations of 100% renewable power global grid by organizing a global grid consortium.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".