Long-term Provincial Load Forecasting In the Context of DERs: A Hybrid Approach
Bibliographic record
Abstract
Accurate long-term load forecasting (LTLF) is essential for strategic resource allocation, infrastructure development, and resilient power system planning, particularly in today’s dynamic power grid landscape shaped by high penetration of Behind-the-Meter (BTM) generation, Distributed Energy Resources (DERs), Electric Vehicles (EVs), and Demand Side Management (DSM). This study proposes a hybrid approach combining a Random Forest (RF) algorithm for trend extraction and an Artificial Neural Network (ANN) for residual modeling to forecast provincial electricity demand over a 20 year horizon. The methodology includes a comprehensive data preprocessing pipeline capable of harmonizing diverse data resolutions and synthesizing future projections under varying development scenarios. Both point forecasts and probabilistic forecasts are produced, where the latter employs Monte Carlo simulations with time-varying noise injection to explicitly capture and communicate long-term uncertainty. The proposed method is practically implemented using NB Power as a case study, achieving a Root Mean Squared Error (RMSE) of 162.80 MW, a Mean Absolute Error (MAE) of $\mathbf{1 1 2. 7 0 ~ M W}$, and a nearzero mean error of -5.36 MW on test data, with $68.5 \%$ overestimations and $31.5 \%$ underestimations. Provided interpretation of results enhances the understanding of future load trajectories and supports evidence-based decisions for long-term utility planning and risk management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".