IoT-Integrated Smart Energy Management for Carbon-Neutral IT Infrastructures
DOI:
https://doi.org/10.54536/ajise.v5i1.6234Keywords:
Carbon-Neutrality, Data Centers, Energy Optimization, Internet of Things, Machine LearningAbstract
The rapid growth of data centers and IT infrastructures has intensified the need for sustainable approaches to operational performance. This study proposes an Internet of Things (IoT)-integrated smart energy management framework that combines forecasting and load optimization to reduce peak demand and carbon emissions. Using the publicly available ElectricityLoadDiagrams2011–2014 dataset, three forecasting models were evaluated: Autoregressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM). Results show that machine learning models significantly outperformed the statistical baseline, with XGBoost achieving the highest accuracy (R² = 0.985). Forecasts were coupled with an IoT-enabled optimizer, which consistently reduced peak load by 10% across seven consecutive test days while shifting approximately 292 megawatt-hours of energy per day, without increasing overall consumption. A carbon footprint analysis under three energy mix scenarios, grid-only, 50% renewable, and 80% renewable, showed reproducible reductions of around 5%, demonstrating both operational and environmental benefits. These outcomes result in reduced stress on critical infrastructure, such as cooling and power distribution units, and deferred capacity upgrades, ultimately leading to meaningful contributions to sustainability goals. The week-long evaluation provides stronger evidence of robustness compared to single-day assessments, and the reliance on open-access data enhances reproducibility and transparency. Overall, the findings demonstrate that IoT-enabled forecasting and optimization can provide a credible, scalable pathway toward carbon-neutral IT infrastructures.
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