Analysis of microclimate temperature and relative humidity distribution of local poultry house in a subtropical area of Nigeria

The design of the ventilation system to ensure microclimate condition are optimum in poultry houses in the Nigerian context requires knowledge of the microclimate parameter distribution, which is lacking in the literature. This study investigated the patterns of temperature and RH distributions in a typical local poultry house. The specific objectives were to (i) analyse the vertical and horizontal distributions of the microclimate parameters in battery cage poultry housing and deep litter poultry housing, (ii) identify whether the distribution is homogenous or heterogeneous, and (iii) identify the data spread of parameters. An experimental intensive naturally ventilated local poultry house was used for this study. It consisted of deep litter (DL) and battery cage (BC) poultry housing systems partitioned by an air wall. Daytime, nighttime, rainy, and dry season temperature and RH distributions in the BC and DL poultry housing were analysed. Approximately 1.2 °C temperature difference was recorded between the poultry house and the ambient environment during the day and night. The temperature and RH distributions in the poultry housing were heterogeneous. Approximately 5% and 67%–73% of the daytime and nighttime temperature data, respectively, and 37%–41% of daytime RH fell within the optimum values

TRNSYS Simulation and Experimental Validation of Internal Temperature and Heating Demand in a Glass Greenhouse

The energy demand in greenhouses is enormous, and high-performance covering materials and thermal screens with varying radiometric properties are used to optimise the energy demand in building energy simulations (BES). Transient System Simulation (TRNSYS) software is a common BES tool used to model the thermal performance of buildings. The calculation of the greenhouse internal temperature and heating demand in TRNSYS involves the solution of the transient heat transfer processes. This study modelled the temperature and heating demand of two multi-span glass greenhouses with concave (farm A) and convex (farm B) shapes. This study aims to investigate the influence of the different BES longwave radiation modes on greenhouse internal temperature in different zones and the heating demand of a conditioned zone. The standard hourly simulation results were compared with the experimental data. The results showed that the standard and detailed modes accurately predicted greenhouse internal temperature (the Nash–Sutcliffe efficiency coefficient (NSE) > 0.7 for all three zones separated by thermal screens) and heating demand (NSE > 0.8) for farms A and B. The monthly heating demand predicted by the simple and standard radiation modes for farm A matched the experimental measurements with deviations within 27.7% and 7.6%, respectively. The monthly heating demand predicted by the simple, standard, and detailed radiation modes for farm B were similar to the experimental measurements with deviations within 10.5%, 6.7%, and 2.9%, respectively. In the order of decreasing accuracy, the results showed that the preferred radiation modes for the heating demand were standard and simple for farm A, and detailed, standard, and simple for farm B

TRNSYS Simulation and Experimental Validation of Internal Temperature and Heating Demand in a Glass Greenhouse

The energy demand in greenhouses is enormous, and high-performance covering materials and thermal screens with varying radiometric properties are used to optimise the energy demand in building energy simulations (BES). Transient System Simulation (TRNSYS) software is a common BES tool used to model the thermal performance of buildings. The calculation of the greenhouse internal temperature and heating demand in TRNSYS involves the solution of the transient heat transfer processes. This study modelled the temperature and heating demand of two multi-span glass greenhouses with concave (farm A) and convex (farm B) shapes. This study aims to investigate the influence of the different BES longwave radiation modes on greenhouse internal temperature in different zones and the heating demand of a conditioned zone. The standard hourly simulation results were compared with the experimental data. The results showed that the standard and detailed modes accurately predicted greenhouse internal temperature (the Nash–Sutcliffe efficiency coefficient (NSE) > 0.7 for all three zones separated by thermal screens) and heating demand (NSE > 0.8) for farms A and B. The monthly heating demand predicted by the simple and standard radiation modes for farm A matched the experimental measurements with deviations within 27.7% and 7.6%, respectively. The monthly heating demand predicted by the simple, standard, and detailed radiation modes for farm B were similar to the experimental measurements with deviations within 10.5%, 6.7%, and 2.9%, respectively. In the order of decreasing accuracy, the results showed that the preferred radiation modes for the heating demand were standard and simple for farm A, and detailed, standard, and simple for farm B

Analysis of Heat and Mass Distribution in a Single- and Multi-Span Greenhouse Microclimate

Recently, heat and mass distributions within a greenhouse were assumed to be homogeneous. Heat is gained or lost in absolute terms, and crop contribution in a greenhouse or its effect is not considered. In this study, statistical analyses were conducted to establish the significance of heat and mass variation at sensor nodes in two single-span and multi-span greenhouses. Three greenhouses were used in this study, 168 m2 floor area a single-layered (SLG), double-layered (DLG) single-span gothic roof type greenhouses, and 7572.6 m2 floor area multi-span greenhouse (MSG). The microclimatic parameters investigated were temperature (T), relative humidity (RH), solar radiation (SR), carbon dioxide (CO2), and vapor pressure deficit (VPD). To check their horizontal distribution, all microclimate data collected from each sensor node in each greenhouse were subjected to descriptive statistics and Tukey honestly significant difference (HSD) test. The lowest minimum temperatures of 2.93 °C, 3.33 °C and 10.50 °C were recorded at sensor points in SLG, DLG, and MSG, respectively, whereas the highest maximum temperatures of 29.17 °C, 29.07 °C and 27.20 °C were recorded at sensor point, in SLG, DLG, and MSG, respectively. The difference between the center and the side into the single-span was approximately 0.88–1.0 °C and in the MSG was approximately 1.03 °C. Significant variation was observed in the horizontal distribution of T, RH, SR, and VPD within SLG, DLG, and MSG. Also significant was CO2 in the MSG. Estimating the energy demand of greenhouses should be done based on the distribution rather than assuming microclimatic parameters homogeneity, especially for T, with VPD as a control parameter. Such estimation should also be done using a crop model that considers instant changes in air and crop temperature

Enhancing sustainable and climate-resilient agriculture: Optimization of greenhouse energy consumption through microgrid systems utilizing advanced meta-heuristic algorithms

Greenhouses offer controlled microclimates that enable year-round cultivation, improving food security and agricultural productivity. However, greenhouses are energy-intensive, with heating accounting for a significant portion of the associated costs. This study explores optimal microgrid configurations, economic viability, and policy recommendations for sustainable greenhouse agriculture in Nigeria. An in-depth energy assessment of a reference greenhouse in a South Korean facility is conducted. Distinct climatic differences between South Korea and Nigeria are highlighted, emphasizing the need for tailored greenhouse designs and energy solutions. Shifting focus to Nigeria, this study investigates the feasibility of hybrid renewable energy systems with a focus on wind and solar power across six geopolitical zones in Nigeria. The analysis encompasses technical, economic, and policy aspects, providing a holistic perspective on renewable energy adoption. Notably, the study uses an advanced optimization model, Teaching and Learning–Based Optimization algorithm, to assess the net present cost and baseload supply reliability, offering valuable insights for investors and policymakers. The result indicates diverse energy requirements across Nigeria, with total monthly peak energy demands ranging from 5374.80 kWh in the Southeast to 17,115.76 kWh in the Northwest, and a notable variation in the Levelized Cost of Electricity (LCOE), with the lowest at $0.07327 in Kano. Specifically, in Ogun, the net present cost for the WT-PV-ESS system stood at$520,935.45, while the PV-ESS system cost was substantially lower at $500,444.41. This confirms the effectiveness of location-specific analysis and shows the suitability of photovoltaic–battery energy storage systems for Nigeria's diverse regions, with unique considerations for specific areas. Policy recommendations, including feed-in tariffs, renewable portfolio standards, net metering, research support, and market development, provide a holistic framework for the adoption of renewable energy and sustainable agriculture. Improving infrastructure, market access, and financing for smallholder farmers is integral for improving food security and standards of living in rural Nigeria. In conclusion, Nigeria can leverage renewable resources to revolutionize its energy and agriculture sectors, setting an example for a sustainable and resilient future