Work

Forecasting and optimization of a residential off-grid solar photovoltaic-battery energy storage system

Year: 2025

Type: article

Abstract: Achieving full energy independence in residential settings remains a challenge with conventional grid-connected solar photovoltaic (PV) systems.This study presents a data-driven approach to designing a grid-independent solar PV and battery energy storage system (BESS) using machine learning techniques.Actual residential load profiles and solar generation data, combined with weather and calendar-based inputs, were analyzed using four regression models: Polynomial Regression, Support Vector Regression, Gradient Boosting, and Random Forest.These models were evaluated based on R², mean absolute error (MAE), and root mean square error (RMSE) to determine the most accurate predictor.The Random Forest model delivered the highest accuracy, achieving an R² of 0.92 for generation and 0.90 for load forecasting.The optimized forecast supported the expansion of the PV system from 11 kW to 30 kW and the integration of a 120 kWh BESS, ensuring complete residential grid independence.This framework offers a reliable and adaptable methodology for intelligent energy management and autonomous PV-BESS system design.

Source: Ecological Engineering & Environmental Technology

DOI: 10.12912/27197050/209482

Authors: Riad Mollik Babu, Md Shahidul Islam, Enamul Basher

Institution: University of Asia Pacific

Language: English

Topic: Smart Grid Energy Management

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: hybrid

Estimated APC paid: $161