AI-Driven Energy Optimization and Storage Management for Smart Grids and Green Buildings

DOI

10.2991/978-94-6239-693-7_38How to use a DOI?

Keywords

Smart Grids; Green Buildings; Energy Optimization; Energy Storage Management; Artificial Intelligence; Renewable Energy

Abstract

Recent technologies for high-energy optimization and storage control have been accelerated by the growing integration of renewable energy sources and smart building systems in modern power systems. The new generation of smart grids and green buildings gives rise to extremely dynamic and heterogeneous streams of information about energy demand, generation, storage, and environmental conditions, so that the conventional rule-based control mechanisms are invalid. This paper suggests an AI-based energy optimization and storage management system, which incorporates predictive analytics, adaptive control, and real-time decision-making to enhance the energy efficiency, reliability, and sustainability of smart grids and green buildings. The suggested structure is that deep learning is used to predict loads and generation, reinforcement learning is used to predict adaptive energy schedules, and the optimization models are used to predict battery storage. A dataset of 3.2 million timestamped records of smart meters, photovoltaic systems, weather stations, and building management systems is used to evaluate the system. Measurement of performance is done based on peak load reduction, energy cost savings, reduction in carbon emissions, efficiency in store utilization, and prediction accuracy. The results of the experiment indicate an optimal load reduction of 26%, a cost saving of 18%, a reduction in carbon emissions of 21%, and greater forecasting accuracy of more than 94% compared to the baseline systems. The efficiency of storage utilization has been enhanced by 23% by use of intelligent charging and discharging strategies. The statistical consideration of the hypothesis proves that the proposed scheme is more effective than traditional optimization methods in all of the considered measures without compromising the stability and responsiveness of the system when the demand variations and the renewable generation conditions change. These findings explain that AI-based energy optimization and storage management can greatly increase the efficiency and sustainability of smart grids and green buildings, helping to turn the city into a low-carbon and energy-efficient environment.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sanjay Kumar
AU  - Sapna Bawankar
PY  - 2026
DA  - 2026/06/16
TI  - AI-Driven Energy Optimization and Storage Management for Smart Grids and Green Buildings
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 375
EP  - 384
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_38
DO  - 10.2991/978-94-6239-693-7_38
ID  - Kumar2026
ER  -