An Optimized Machine Learning Approach for Electricity Price Prediction in Cloud Data Centers
Bibliographic record
Abstract
Cloud computing has revolutionized the IT sector as physical infrastructure dependence is minimized, but the energyconsuming nature of the large-scale data center has led to electricity being a critical issue. The recent dynamism in electricity prices makes the effective management of resources in cloud environment more difficult. In a bid to resolve this problem, this paper suggests an improved machine learning model on the problem of electricity price prediction and efficient allocation of resources using Extreme Gradient Boosting (XGBoost). The model is also created to enhance the location of data and scheduling of nodes that decreases the use of energy and operational costs. An actual dataset used to evaluate it is of the Independent Electricity System Operator (IESO), Ontario, Canada, where data are divided into 70 percent of training and 30 percent of testing. The experimental findings prove that the suggested method provides correct prediction of electricity prices and allows cloud data centers to schedule their activities energy-consciously. This makes cloud computing infrastructures more sustainable, cost-efficient and environmentally friendly.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".