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International Journal of Advanced Engineering Application

ISSN: 3048-6807

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Design and Performance Analysis of Campus Scale Solar Energy Storage Systems Using Predictive Analytics

Author(s):Prof. Rajesh V. , Dr. Aminah Binti Zainal

Affiliation: Department of Electrical Engineering, Vidya Jyoti Institute of Technology, Hyderabad, India

Page No: 42-47

Volume issue & Publishing Year: Volume 3, Issue 2, 2026-02-22

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI: https://doi.org/10.5281/zenodo.18814114

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Abstract:
As global energy markets transition toward decentralized architectures, regional educational institutions in emerging economies face significant challenges in balancing rising operational costs with 2030 sustainability mandates. This research addresses the critical "temporal mismatch" between peak photovoltaic (PV) generation and campus-wide energy demand by introducing a localized, intelligent microgrid framework. Unlike traditional Battery Energy Storage Systems (BESS) that utilize reactive, threshold-based control logic, this study proposes a predictive management architecture driven by a Long Short-Term Memory (LSTM) neural network.
We deployed a 50kW monocrystalline PV array coupled with a 100kWh Lithium Iron Phosphate (LiFePO4) storage bank at a regional engineering college to evaluate the efficacy of "weather-aware" and "load-aware" dispatch strategies. The LSTM model was trained on 24 months of high-resolution campus consumption data and 2025 meteorological records to perform a rolling 12-hour forecast of energy requirements.
Our empirical results demonstrate that the predictive framework enabled a 19% reduction in peak-peak demand charges by strategically discharging the BESS during high-tariff windows. Furthermore, the system improved solar self-consumption from a baseline of 62% to 78%, ensuring maximum utilization of generated green energy. Crucially, the predictive logic mitigated the occurrence of "micro-cycling," resulting in a 32% reduction in unnecessary battery stress and an estimated extension of the storage asset’s operational life by 2.5 years. By localizing the computational workload on an edge-computing server, the system maintained a 99.2% operational uptime, proving that sophisticated, AI-driven energy autonomy is technically feasible and economically viable for regional institutions without reliance on expensive, cloud-based industrial infrastructure. This study provides a scalable blueprint for transforming educational campuses into self-sustaining grid-anchors within their local communities.

Keywords: Solar Energy Storage; Photovoltaic Systems; Battery Energy Storage Systems (BESS); Predictive Analytics; Campus Microgrids; Energy Independence.

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