Electricity Price Forecasting for Cloud Computing Using an Enhanced Machine Learning Model

• Main Authors: Saleh Albahli, Muhammad Shiraz, Nasir Ayub
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Data storage; energy saving; electricity price forecasting; XGBoost
• Online Access: https://ieeexplore.ieee.org/document/9246503/

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.