Long Term Electricity Load Forecast Based on Machine Learning for Cameroon’s Power System
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Bibliographic record
Abstract
A reliable power supply has long been identified as an important economic growth parameter. Electricity load forecasts predict the future behavior of the electricity load. Carrying out a forecast is important for real-time dispatching of power, grid maintenance scheduling, grid expansion planning, and generation planning depending on the forecasting horizon. Most of the methods used in long-term load forecasting are regressions and are limited to predicting peak loads of a yearly or monthly resolution with low accuracy. In this paper, we propose a method based on long short-term memory-recurrent neural networks (LSTM-RNN) cells with relations between identified influential econometric load-driving parameters which includes: the Gross Domestic Product (GDP), Population (H), and past Electric Load Data. To the best of our knowledge, the use of the GDP and H as two additional independent variables in load forecast modelling using machine learning techniques is a novelty in Cameroon. A comparison was performed between a linear regression (LR)-based long-term load forecast model (a model currently used by the Transmission System Operator of Cameroon) and LSTM-RNNs model constructed. The results generated were evaluated using a Mean Absolute Percentage Error (MAPE) within the same period of evaluation, and the overall value of the MAPE obtained for LSTM-RNNs model was 5.4962 whereas that for the LR model was 7.5422. Based on these results, the LSTM-RNN model is considered highly accurate and competent. The model was used to generate a forecast for the period of 2022–2026 with an hourly resolution. A MAPE of 5.4962 was obtained with a computational time of approximately ten minutes, making the model vital for offline use by utilities due to its capacity to quantitatively and accurately predict long-term load with an hourly resolution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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