Optimizing energy forecasts at Boma for 2023 to 2053 Using machine learning techniques of the PSO algorithm
Bibliographic record
Abstract
This research was conducted to optimize energy consumption forecasting in the commune of Boma, in the Democratic Republic of Congo, in the face of persistent imbalances between energy production and demand. The main objective of the study was to assess local energy needs in order to support the economic and social development of the region. To achieve this objective, a methodology integrating quantitative and qualitative techniques was adopted. Data were collected through surveys conducted among residential, semi-industrial, and tertiary consumers, as well as demographic information provided by the town hall. In parallel, machine learning techniques were employed to predict energy consumption, with the Particle Swarm Optimization (PSO) algorithm used to optimize forecasts. The forecasting model was accompanied by statistical analyses, including the Pearson correlation coefficient and the Student t-test, to validate the results. The analysis revealed a very high correlation between actual and predicted values, with a coefficient reaching 0.999, which demonstrates high model accuracy. However, biases were observed, including a tendency to overestimate energy consumption, highlighting the importance of reliable data collection to improve forecast accuracy. In conclusion, the PSO algorithm has proven to be an effective tool for energy demand management, although adjustments are necessary to optimize the results. The lessons learned highlight the need for a thorough understanding of consumption behaviors and regular data updates to adapt forecasts to future developments.Keywords: Optimization, energy forecasting, PSO algorithm, machine learning techniques, energy management
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 | 0.000 | 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 | 0.000 | 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 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".