Hasila Jarimi

Thermoelectric (TE) heating, ventilation, and air-conditioning (HVAC) systems represent a sustainable alternative to conventional vapor-compression technologies; however, their adoption is limited by relatively low coefficients of performance (COP) and challenges in performance optimization. This study presents an integrated experimental, modelling, and deployment framework for predicting and evaluating TE HVAC performance. A custom-built test rig was developed to generate experimental data under controlled laboratory conditions, capturing key thermodynamic variables. A total of 487 samples were used to benchmark multiple regression and machine learning models. Among the evaluated approaches, Linear Regression achieved the best performance, with a root mean squared error (RMSE) of 3.28 and a coefficient of determination (R²) of 0.885, outperforming more complex models. Beyond statistical evaluation, model predictions were validated through recomputation of heat transfer rate and COP, demonstrating strong thermodynamic consistency and physical interpretability. The validated model was subsequently implemented within a Shiny-based graphical interface, enabling interactive scenario analysis and real-time performance evaluation. The results demonstrate that, for moderate-scale experimental datasets, simple and interpretable models can outperform more complex approaches while maintaining physical consistency. The proposed framework provides a practical pathway for integrating experimental data, predictive modelling, and decision-support tools in thermoelectric HVAC applications.

The transition to low-carbon heating in the UK requires large-scale electrification of space and water heating, supported by compact and flexible thermal storage to balance variable renewable electricity supply and heat demand. This work investigates a binary sugar-alcohol–based supercooled phase change material (PCM) as a controllable thermal battery for low-temperature heating systems. The PCM is designed to melt in the 50–90 °C range, providing useful discharge temperatures for radiators and domestic hot water while exploiting long-duration supercooling for deferred heat release. The study was conducted in three stages. First, material screening was performed using T-history analysis to identify suitable binary sugar-alcohol mixtures with high latent heat, stable supercooling, and peak crystallisation temperatures in the target range. A non-eutectic mixture of xylitol–erythritol (Xy–Er) was selected, delivering a cumulative enthalpy of ~287.78 kJ kg⁻¹ under triggered conditions while remaining stably supercooled after cooling to ambient. Second, four triggering strategies were systematically evaluated, namely seeding, localised cooling using a thermoelectric (TEC) heat sink, electrode-based activation, and mechanical agitation, including the effect of prior cold crystallisation (reheating to 60 °C). Third, in situ crystallisation image-processing analysis was combined with an Avrami-type (JMAK) model with fixed exponent n=0.5 to quantify effective crystallisation kinetics. The results show that mechanical agitation and electrode triggering at 60 °C outperform seeding and TEC-based cooling, with mechanical agitation yielding the highest effective rate parameters. Meanwhile, TEC-based cooling in the present configuration was insufficient to produce significant crystallisation. These findings demonstrate that appropriately screened sugar-alcohol mixtures, combined with practical triggering methods, can enable controllable supercooled PCMs to be used as on-demand thermal batteries for future low-carbon heating systems.

Addressing global energy challenges, particularly energy poverty, climate change, and energy security, requires integrated approaches that combine technological innovation with policy, finance, and implementation strategies. Energy Catalyst (EC) continues to provide an international, multidisciplinary platform for high-quality research and applied solutions that advance sustainable and inclusive energy systems. This editorial outlines the journal’s scope, editorial priorities, and continued commitment to supporting research that enables reliable, affordable, and sustainable energy access worldwide.

With the increasing global demand for cooling and the rising environmental concerns associated with conventional cooling systems, solar-powered cooling technologies have emerged as a sustainable alternative. This study provides a comprehensive review of recent advancements in solar-powered cooling systems, highlighting the latest technological developments, challenges, and future directions. The paper classifies solar cooling technologies into two primary categories: Solar electric cooling and solar thermal cooling. It discusses various configurations, including PV-assisted vapour compression systems, solar absorption and adsorption cooling system, and hybrid approaches that integrate thermoelectric and thermal cooling mechanisms. The findings indicate that while solar cooling systems offer significant energy savings and carbon emission reductions, several technical, economic, and operational challenges remain. Key issues include the intermittency of solar energy, efficiency limitations of current photovoltaic and thermal conversion technologies, and high initial investment costs. Moreover, the effectiveness of different solar cooling methods varies depending on climatic conditions and building types, necessitating further research on system optimisation and scalability. Future research should focus on improving photovoltaic efficiency through advanced materials such as perovskite-silicon tandem cells, enhancing thermal storage systems, and integrating artificial intelligence-based optimisation for real-time energy management. Besides, hybrid cooling approaches that combine passive and active strategies, along with emerging energy storage solutions, could further enhance the viability of solar cooling technologies. By addressing these challenges, solar-powered cooling systems can play a crucial role in reducing energy consumption in the building sector and contributing to global sustainability goals.

Thermochemical energy storage (TES) is emerging as a transformative solution for enhancing thermal energy management in buildings and industrial applications. Unlike conventional sensible and latent heat storage, TES systems store energy through reversible chemical reactions, offering significantly higher energy densities and minimal standby losses. This review provides a comprehensive overview of recent developments in TES materials and system configurations, including both closed-cycle systems (e.g., chemical heat pumps, adsorption/absorption cycles) and open-cycle systems integrated with solar thermal collectors. Advances in material engineering—particularly in salt hydrates, composite sorbents, and nanostructured materials—have led to improvements in cyclic stability, regeneration temperature, and thermal efficiency. The paper also highlights innovative reactor designs such as fixed-bed, fluidized-bed, and plate-fin heat exchangers, alongside smart control and additive manufacturing approaches. Key technical challenges, including reaction kinetics, long-term stability, and heat/mass transfer complexity, are discussed to identify future research needs. This review aims to support the development of high-performance, scalable TES systems for sustainable and flexible energy infrastructure.

Accurate and reliable forecasting is critical for sustainable energy grid planning, as fluctuating demand, market volatility, and policy uncertainty pose significant challenges to both operational and financial stability. This paper proposes a novel AI-based hybrid forecasting framework that integrates Facebook Prophet, for interpretable long-term trend and seasonality decomposition, with Long Short-Term Memory (LSTM) networks, for modelling nonlinear short-term residual dynamics. This design explicitly addresses the dual requirement of transparency and high predictive accuracy in complex, non-stationary energy systems. The framework is evaluated using 18 years of historical financial data from Tenaga Nasional Berhad (TNB), Malaysia’s largest electricity utility, as a representative large-scale grid operation case study. Performance is benchmarked against ARIMA, standalone Prophet, and standalone LSTM models using RMSE, MAE, MAPE, and SMAPE, with statistical significance assessed via the Diebold–Mariano test and robustness examined under varying forecast horizons and noise perturbations. Results show that the proposed hybrid Prophet–LSTM model achieves up to 15% lower RMSE and MAPE than the best-performing baseline while maintaining stable performance under adverse conditions. The findings demonstrate that the proposed framework provides a robust, interpretable, and modular decision-support tool for utility operators, energy planners, and policymakers, enabling improved financial optimisation, tariff planning, and operational resilience in sustainable energy grid systems.

Artificial Intelligence (AI) revolutionizes the renewable energy sector by enabling advanced forecasting, real-time optimization, and autonomous system control. As global efforts toward decarbonization intensify, AI applications have rapidly expanded across various renewable energy domains. However, the literature remains fragmented, lacking a focused synthesis of evolving AI techniques and their domain-specific implementations. This study addresses this gap by systematically reviewing AI applications in three critical energy sectors: Solar Energy, Wind Energy, and Energy Storage & Smart Grids. Using the PRISMA methodology, peer-reviewed articles published between 2015 and 2025 were extracted from two authoritative databases—IEEE Xplore and ScienceDirect. The selected studies were classified based on AI methods, including machine learning, deep learning, reinforcement learning, fuzzy logic, and emerging paradigms such as explainable AI (XAI), generative AI, graph neural networks (GNNs), and physics-informed neural networks (PINNs). Key contributions of this review include a cross-source comparative analysis, domain-specific trend mapping over a decade, and the identification of gaps in methodological transparency. Findings reveal increasing use of hybrid models, growing interest in interpretable and physically grounded techniques, and persistent underreporting of AI methodologies in the literature. This review provides actionable insights and research directions toward developing intelligent, explainable, sustainable energy systems.