On the Energy Consumption Forecasting of Data Centers Based on Weather Conditions: Remote Sensing and Machine Learning Approach

TL;DR

This paper explores using weather data and machine learning to predict data center energy consumption, aiming to improve energy management and support renewable energy integration.

Contribution

It introduces a method combining weather data with linear regression for accurate energy consumption forecasting in data centers.

Findings

Weather conditions significantly correlate with data center energy use.

The proposed model effectively predicts energy consumption using weather forecasts.

Results support better energy management and grid optimization.

Abstract

The energy consumption of Data Centers (DCs) is a very important figure for the telecommunications operators, not only in terms of cost, but also in terms of operational reliability. A relation between the energy consumption and the weather conditions would indicate that weather forecast models could be used for predicting energy consumption of DCs. A reliable forecast would result in a more efficient management of the available energy and would make it easier to take advantage of the modern types of power-grid based on renewable energy resources. In this ,paper, we exploit the capabilities provided by the FIESTA-IoT platform in order to investigate the correlation between the weather conditions and the energy consumption in DCs. Then, by using multi-variable linear regression process, we model this correlation between the energy consumption and the dominant weather conditions parameters in order to effectively forecast the energy consumption based on the weather forecast. We have validated our results through live measurements from the RealDC testbed. Results from our proposed approach indicate that forecasting of energy consumption based on weather conditions could help not only DC operators in managing their cooling systems and power usage, but also electricity companies in optimizing their power distribution systems.