Journal of Renewable Energy and Smart Grid Technology

Modeling and Real-World Validation of Home Energy Management Systems: Photovoltaic Generation, Battery Energy Storage, and Bidirectional Grid Connection

Supakan Janthong — 2026-07-13

This article presents comprehensive modeling, simulation, and real-world validation of a Home Energy Management System (HEMS) using MATLAB/Simulink. The model integrates photovoltaic (PV) panels with single-diode equivalent circuits, lithium-ion battery energy storage systems (BESS) with electrochemical modeling, unidirectional boost converters for PV voltage regulation, bidirectional DC/DC converters for battery control, bidirectional DC/AC inverters with sinusoidal PWM for grid synchronization, and utility grid connection to enhance energy efficiency and reduce grid dependency. Five operational scenarios were designed and tested: (1) normal operation with PV as primary source, (2) high load exceeding PV capacity, (3) low solar irradiance requiring battery support, (4) battery charging during PV surplus, and (5) grid export of excess energy. Each scenario was analyzed under varying environmental conditions (solar irradiance 0–650 W/m²) and load profiles. Simulation results demonstrate effective energy balancing through priority-based control (PV → Battery → Grid), with response times under 200 ms, DC bus voltage stability within ±2%, and AC power quality meeting grid standards (THD < 3%, power factor > 0.98). Real-world validation used 30 days of operational data from a 5 kW PV system with 10 kWh battery storage monitored through the FusionSolar platform. Experimental results showed strong model accuracy with
an average MAPE of 4.2%. Performance metrics demonstrated 65.2% PV self-consumption under normal conditions, 37% peak demand reduction during high-load scenarios, 92.4% battery charging efficiency, and 96.1% grid export inverter efficiency. The validated model confirms the HEMS performance evaluation and demonstrates the practical feasibility and economic viability of integrated PV-battery-grid systems.

A Review of Machine Learning Models and Evaluation Techniques for Single String Anomaly Detection in Malaysian Large-Scale Photovoltaic Systems

Muhammad Ashraf ฺBin Ghazali — 2026-07-03

The rapid expansion of solar photovoltaic (PV) systems has increased the need for reliable and intelligent fault detection techniques to maintain system performance and operational efficiency. This paper presents a comprehensive review of machine learning (ML) models and evaluation techniques for single-string anomaly detection in large-scale PV systems, with particular emphasis on applications in Malaysia’s tropical environment. The review examines the transition from conventional monitoring approaches, such as I–V curve tracing and threshold-based diagnostics, to advanced data-driven methods. Various ML and deep learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and hybrid frameworks such as CNN–SVM and AE–GRU, are analysed in terms of their anomaly detection capabilities and performance. Common evaluation metrics, including RMSE, MAE, MAPE, R², and F1-score, are also reviewed to assess model effectiveness and reliability. In addition, recent Malaysian studies employing K-Means–LSTM clustering, Random Forest-based fault diagnosis, Vision Transformers (ViT) and Vertices Principal Component Analysis (VPCA) are discussed, highlighting their potential for PV anomaly detection under tropical operating conditions. The review identifies key challenges, particularly the dependence on labelled datasets, which limits scalability and early fault detection. Emerging opportunities in unsupervised and semi-supervised learning approaches, including autoencoders, Isolation Forests, and clustering-based reconstruction methods, are also explored as promising solutions for real-time and adaptive anomaly detection. The findings provide insights into current research trends, existing limitations, and future directions for enhancing the reliability and maintenance of large-scale PV systems in Malaysia.

Comparative Review of Bio-Oil and Liquid Smoke in Terms of Characteristics, Composition, and Applications

Delphy Yustisia Ayu Praja — 2026-07-09

Thermal decomposition process or commonly referred as pyrolysis is one of the methods that can be utilized to obtain several chemical products. Through the pyrolysis process, there are three products produced, each of which can be used; char, tar and bio-oil or liquid smoke. This article will specifically discuss about bio-oil and liquid smoke which are often assumed as the same, but are actually two different things. It is based on the used feedstock, composition of compounds in the product and the utilization of both which are different. To support the writing of the article, a Systematic Literature Review (SLR) method was used to obtain data to analyze and critically compare the differences between bio-oil and liquid smoke. The findings indicate that the used feedstock may affect the quality and characteristics of pyrolysis liquid products. Liquid products obtained from feedstocks contained organic compounds (e.g. biomass) tend to produce high acid and phenol content, called as liquid smoke. While liquid products produced from feedstocks with long carbon chain bonds (e.g. several types of plastics and scrap tires) tend to produce high hydrocarbon compounds called as bio-oil.