Battery Monitoring and Energy Forecasting for an Off-Grid Solar Photovoltaic Installation

2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) Pub Date : 2019-01-01 DOI:10.1109/ROBOMECH.2019.8704728

Sailen Nair, William Becerra Gonzalez, J. Braid

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引用次数: 2

Abstract

This document details the design and implementation for an off-grid PV energy management system with key objectives being battery monitoring and energy forecasting. Coulomb counting is used to estimate the state of charge of a 24 V, 100 Ah lead-acid battery bank. An ACS712 hall effect current sensor module is used to implement the coulomb counter. A random forest regression model is used to predict solar irradiance with a root mean square error of 15.4%, when comparing actual Johannesburg irradiance data to predicted data. Energy forecasting is successfully done with a root mean square error of 6.2% for a three-day continuous test. It is concluded that the proposed solution successfully demonstrates a working principle for an energy management system, however, further improvements are needed to make the forecast model as accurate as possible.

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离网太阳能光伏装置的电池监测与能量预测

本文详细介绍了离网光伏能源管理系统的设计和实现，主要目标是电池监测和能源预测。库仑计数用于估计24 V, 100 Ah铅酸蓄电池组的充电状态。采用ACS712霍尔效应电流传感器模块实现库仑计数器。采用随机森林回归模型预测太阳辐照度，将约翰内斯堡实际辐照度数据与预测数据进行比较，均方根误差为15.4%。在连续3天的测试中，能源预测的均方根误差为6.2%。结果表明，所提出的解决方案成功地展示了能源管理系统的工作原理，但需要进一步改进以使预测模型尽可能准确。

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2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA)

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