Electricity Price Forecasting for Cloud Computing Using an Enhanced Machine Learning Model
Bibliographic record
Abstract
Cloud computing is rapidly taking over the information technology industry because it makes computing a lot easier without worries of buying the physical hardware needed for computations, rather, these services are hosted by companies with provide the cloud services. These companies contain a lot of computers and servers whose main source of power is electricity, hence, design and maintenance of these companies is dependent on the availability of steady and cheap electrical power supply. Cloud centers are energy-hungry. With recent spikes in electricity prices, one of the main challenges in designing and maintenance of such centers is to minimize electricity consumption of data centers and save energy. Efficient data placement and node scheduling to offload or move storage are some of the main approaches to solve these problems. In this article, we propose an Extreme Gradient Boosting (XGBoost) model to offload or move storage, predict electricity price, and as a result reduce energy consumption costs in data centers. The performance of this method is evaluated on a real-world dataset provided by the Independent Electricity System Operator (IESO) in Ontario, Canada, to offload data storage in data centers and efficiently decrease energy consumption. The data is split into 70% training and 30% testing. We have trained our proposed model on the data and validate our model on the testing data. The results indicate that our model can predict electricity prices with a mean squared error (MSE) of 15.66 and mean absolute error (MAE) of 3.74% respectively, which can result in 25.32% cut in electricity costs. The accuracy of our proposed technique is 91% while the accuracy of benchmark algorithms RF and SVR is 89% and 88%, respectively.
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 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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".