An Optimized Machine Learning Approach for Electricity Price Prediction in Cloud Data Centers

Abstract

Cloud computing has revolutionized the IT sector as physical infrastructure dependence is minimized, but the energy-consuming 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.

Introduction

Cloud computing data centers consume significant electricity due to growing computational demands. Since electricity prices are highly variable and differ by region, managing energy use efficiently has become crucial for cost savings and environmental sustainability. Accurate electricity price forecasting helps optimize operations like workload scheduling and data placement.

2. Problem Statement

Traditional forecasting models (e.g., ARIMA) struggle with the non-linear, volatile nature of electricity markets. While deep learning models (like LSTM and CNN-RNN) offer better accuracy, they often require high computational resources, making real-time integration difficult. There's a gap in integrating electricity price forecasting directly into cloud computing optimization strategies.

3. Proposed Solution

This study proposes using XGBoost, a scalable and efficient machine learning model, for accurate electricity price forecasting using real-world data from Ontario’s Independent Electricity System Operator (IESO).

Key contributions include:

• Electricity price forecasting using XGBoost.

• Integration of predictions into cloud data center operations for:

  • Node scheduling

  • Data placement

  • Dynamic resource allocation

• Improved energy efficiency and reduced costs.

4. Methodology Overview

A. Data Preprocessing

• Source: Hourly IESO data (price, demand, weather).

• Techniques: Data cleaning, feature engineering (e.g., lag values, time-based patterns).

• Train-test split: 70:30 with K-fold cross-validation.

B. XGBoost Model Design

• Uses gradient-boosted decision trees with regularization.

• Hyperparameters tuned using grid search and Bayesian optimization.

C. Cloud Optimization Techniques

• Node Scheduling: Schedule workloads during predicted low-tariff periods.

• Data Placement: Shift data-intensive tasks to cheaper regions.

• Resource Allocation: Dynamically scale VMs based on forecasts.

D. Evaluation Metrics

• Forecasting Accuracy: RMSE, MAE, MAPE.

• Optimization Effectiveness: Energy cost savings, efficiency improvements, workload balancing.

5. Literature Review Insights

A survey of 10 recent studies on electricity price forecasting showed:

• Evolution from traditional methods (ARIMA) to ML and DL (LSTM, Transformers, GNNs).

• Common gaps: generalization across regions, high computational cost, poor integration into cloud resource scheduling.

6. Key Findings & Contributions

• The XGBoost model outperforms older statistical methods in accuracy and speed.

• Demonstrated benefits in operational cost reduction and sustainability for cloud computing.

• The model can be deployed with less overhead than deep learning alternatives.

Conclusion

The electricity price forecasting model proposed based on XGBoost has shown good predictive power, with a high R 2 value of 91 percent and high performance in comparison with the traditional XGBoost models including Random Forest and Support Vector Regression. With the combination of forecasting and cloud data center optimization strategies such as node scheduling, data placement and dynamic resource allocation, the system is successful in lowering the cost of operation, enhancing energy and making a contribution to sustainable cloud computing infrastructures. These findings confirm that XGBoost approach is accurate and scalable, hence it is applicable in real world applications with respect to cost sensitive environment in clouds.

References

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Copyright

Copyright © 2025 Vechalapu Anjali Priyanka , K. Tulasi Krishna Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.