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

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 efficient data placement and node scheduling to offload or move storage are some of the maintenance of such centers is to minimize electricity consumption of data centers and save energy. Ein approaches to solve these problems.

 In this project, 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.

Keywords: Data Storage, Energy Saving, Electricity Price Forecasting, XGBoost.