The daily dependence on firewood by households in developing countries creates continuous demand for fuel that often outpaces natural drying times prior to use. Given that high moisture content (˃20%) in firewood causes incomplete combustion, which is associated with adverse health impacts, this study assessed whether seasonal climatic variability theoretically supports firewood drying to acceptable fuel quality standards and how household storage practices influence this potential. Data were collected from 128 households using a structured questionnaire on preferred firewood species, storage practices, moisture content indicators, alongside regional climate data for the year 2025. The equilibrium moisture content (EMC) and net calorific value (NCV) were theoretically modeled under equilibrium assumptions to estimate the theoretical fuel quality under the prevailing climatic conditions. Three hardwoods: Dichrostachys cinerea , Combretum zeyheri , and Combretum apiculatum were most preferred. Outdoor uncovered storage dominated in both the dry (77%) and wet (67%) seasons. Climate analysis indicated that the prevailing climatic conditions possess the theoretical capacity to support high-quality fuel under equilibrium assumptions with NCV ranging from 15.63 to 16.68 MJ/kg, thus meeting ISO 17225–5 A1 classification in both seasons. However, continued reliance on outdoor uncovered storage, even in the wet season, increases the likelihood of moisture reabsorption and prolonged drying. Consequently, the realized fuel quality is likely constrained by storage practices more than environmental limitations. This highlights the need for climate-adaptive storage interventions to improve the combustion efficiency and reduce exposure to household air pollutants without reliance on costly technologies.
Agrivoltaics involves a trade-off between the negative impact on shade-intolerant crop growth caused by panel shading and the positive impact of electricity generation. Although taro is known as a shade-tolerant crop, the effect of shade from the panels on reducing soil temperature and the effect of installing reflective sheets to increase bilateral photovoltaic power generation and reduce soil temperature have not been verified. This study used an experimental field with wooden panels that shade solar radiation and reflective sheets that strongly reflect solar radiation and examined the effects of shade and reflective sheets. It shows that the land equivalent ratio (LER) can be significantly greater than one in high shade plots (GCR, ground cover 29%). The reflective sheets not only estimated to increase the amount of electricity generated by approximately 20%, but also improved taro yield by lowering soil temperature. For panels with a GCR of 29%, the LER improves from 1.65 to 2.12 in 2021 and from 1.90 to 1.94 in 2023 due to the reflective sheets. Even under a lower GCR of 14% without reflective sheets, the LER exceeded 1.0, reaching 1.42 in 2025. Box–Cox-transformed random-intercept linear mixed-effects models showed significant associations of quantitative GCR, reflective-sheet treatment, and experimental year with taro yield, together with a strong reflective-sheet × year interaction. The findings indicate that shade has the positive effect of reducing midsummer soil temperature, demonstrating that shade has an additional positive effect on the yields of shade-tolerant crops. Agrivoltaics of taro using reflective sheets has shown promise as a system that can sustainably produce both crops and electricity.
The performance test results reported on washing machine energy labels are averaged from tests conducted under half-load and full-load conditions, which may differ substantially from the half-load conditions more commonly encountered in actual use. In addition, correlations among different performance parameters may undermine the scientific basis for applying a uniform energy-efficiency threshold as an evaluation criterion. In this work, the effects of load capacity on cleaning performance, water extraction performance, rinsing performance, and energy and water consumption results were investigated. The results show that load capacity affects different performance metrics of the two washing machine types to varying degrees. For the conventional top-loading washing machines, load capacity had the greatest impact on rinsing performance. For the front-loading washing machine, water consumption and cleaning score were the indicators most significantly affected by load capacity. In addition, the intrinsic relationships among the five performance parameters were analyzed. For conventional top-loading and front-loading washing machines, the two pairs of performance metrics exhibiting the strongest correlations were energy consumption and residual moisture content, and energy consumption and water consumption, with correlation coefficients of − 0.69 and 0.90, respectively. However, when mini washing machines were included, the correlations among the performance parameters changed significantly, with water consumption and cleaning score becoming the pair of performance metrics showing the strongest correlation. This study can provide a reference for adjusting the weighting of half-load conditions in future standard revisions, establishing differentiated reference thresholds for energy-efficiency grades according to washing machine types, and guiding manufacturers in improving washing performance.
This paper investigates the multimodal P–V characteristics of novel symmetric photovoltaic (PV) modules subjected to partial shading conditions arising from sunrise, sunset, and dynamic shadow propagation, which impede maximum power point tracking (MPPT) and induce substantial power yield degradation. To address these challenges, an autonomous current compensation strategy employing a zeta converter is proposed for shaded PV submodules. The methodology was modeled and validated through simulation using the advanced power-electronics design platform PLECS. Simulation and verification outcomes confirm the approach’s viability and superior conversion efficiency. When integrated with a conventional perturb-and-observe algorithm and a buck converter-based MPPT architecture, the current compensation scheme attains an average overall power-generation efficiency of 97.78
The growing demand for electricity, heating, and cooling, coupled with rising fuel costs and environmental concerns, has intensified interest in hybrid and multi-energy systems capable of integrating renewable and conventional energy sources. This research work presents a hybrid solar photovoltaic–Combined Heat and Power–biomass (PV–CHP–biomass) energy system integrated with a battery energy storage system for cost-optimal energy management in rural and semi-industrial applications. A Firefly Algorithm (FA)-based stochastic optimization framework is developed to address photovoltaic intermittency using real solar irradiance data and time-of-use electricity tariffs. The proposed hybrid system integrates a 3.5 kW photovoltaic array, 700 kW combined heat and power (CHP) unit, 1000 kW boiler, 500 kWh battery energy storage system, and thermal storage facilities to simultaneously satisfy electricity, heating, and cooling demands of an abattoir facility. Simulation results obtained using Python and the Pymoo library demonstrate that the proposed intelligent scheduling strategy significantly improves system operational efficiency and economic performance. Comparative scenario analysis indicates that operational costs can be reduced by approximately 92.97
