Applications of Regression Enhanced Self-Organizing Incremental Neural Networks (RE-SOINN) in an embedded intelligent energy control system for off-grid systems - Puah, Boon Keat (2024)
Battery Energy Storage System (ESS) is a very important component in most of the off-grid standalone photovoltaic (PV) system as battery ESS can store excess unused solar energy harvested via solar panels for later use. However, normal household electronic appliances often cause irregular load demand, pushing the battery ESS to experience deep power discharge cycles. The intermittent nature of solar irradiance has also contributed to irregular charging patterns of battery ESS. Therefore, battery-supercapacitor Hybrid Energy Storage System (HESS) is introduced as the most promising solution to reduce the harms to the battery. Most of the control strategies in the literature focus on the optimization of the system operations. For instance, the control strategy is usually implemented to optimally manage HESS based on real-time operating conditions. Though there are applications of intelligent energy control system in the past literature, there are still limited studies on how solar irradiance prediction is being embedded into the energy management system (EMS). Furthermore, many solar irradiance forecasting solutions provided in the literature are based upon deep learning algorithms which require a lot of computational resources, impeding these forecasting solutions to be implemented in off-grid applications. Also, the EMS implemented in the literature are based upon Artificial Intelligence-based optimization algorithms which use iterative computations to reach to optimal decisions making. As a result, an incremental unsupervised learning-based EMS is developed to perform solar irradiance forecasting as well as to manage and control the system operations in an off-grid standalone PV Renewable Energy Power System (REPS). The objectives of this research study are to develop a computational efficient hourly incremental unsupervised learning solar irradiance forecasting model using basic features as well as to develop and to implement an incremental unsupervised learning EMS in an actual standalone PV system with battery-supercapacitor HESS.