Optimizing energy consumption through AI and cloud analytics: Addressing data privacy and security concerns

The escalating global demand for energy efficiency has underscored the critical need for intelligent, data-driven strategies to optimize energy consumption across sectors. This research investigates the integration of artificial intelligence (AI) and cloud-based analytics platforms to facilitate energy optimization while concurrently addressing the imperative challenges of data privacy and security. By leveraging machine learning algorithms and predictive modeling, AI enables real-time monitoring, forecasting, and adaptive control of energy usage patterns. Cloud analytics, with its scalable computational capabilities, further enhances decision-making processes through the aggregation and analysis of vast and heterogeneous datasets. However, the centralization of sensitive energy consumption data introduces significant risks related to data breaches, unauthorized access, and regulatory non-compliance. This paper presents a comprehensive examination of privacy-preserving AI models, federated learning architectures, encryption techniques, and secure multi-party computation methods that collectively mitigate these concerns. The study also explores practical implementations and policy considerations necessary for the secure deployment of AI-driven cloud analytics in energy systems.