Faustine Kasongi Enos
Stipendiat
(+47) 403 19 443
faustine.enos@nibio.no
Sted
Særheim
Besøksadresse
Postvegen 213, NO-4353 Klepp stasjon
Sammendrag
Greenhouse cultivation can help meet food demand in a growing and increasingly urbanised population. Reliance on fossil-fuel heating and natural ventilation often makes conventional greenhouses energy- and carbon-inefficient. Closed greenhouses address these limitations through resource recycling and energy recovery. While a centralised environmental control system (ECS) integrating climate control and heat harvesting has shown potential to improve greenhouse crop performance at high latitudes, its year-round energy use and energy-related carbon footprint reduction potential remains insufficiently quantified. This study extends an existing dynamic greenhouse climate model to incorporate a novel centralised ECS integrating air recirculation, heating, cooling, and heat harvesting in (semi-)closed greenhouses. The model was validated using experimental data from Norway, reproducing temperature and relative humidity with RMSEs of 1.40–1.63 °C and 7.60–8.55%, respectively. Energy use and tomato yield were predicted with relative errors of 3.8–8.4% and 1.6–4.2%, respectively. Scenario simulations under Norwegian conditions showed that (semi-)closed greenhouses with heat harvesting can reduce fossil fuel use by over 80% while increasing tomato yields by 15–41% relative to open greenhouses, driven by changes in CO2 concentration and temperature following reduced ventilation and heat recovery. The performance of a fully closed greenhouse relying solely on on-site cold storage is constrained by cooling capacity and buffer size, particularly during summer; adding a supplemental cold energy source such as surface water can improve its performance. Despite heat harvesting, a residual boiler heating demand of 3–10% remains. Further gains in energy efficiency and crop performance may be achieved through optimised climate control.
Forfattere
Durrotun Nashihin Sumarni Sumarni Ratu Mauladaniyati Khoirul Islam Saifudin Yahya Faustine Kasongi EnosSammendrag
Achieving SDG 4 on improving quality education requires higher education institutions to adopt technology-enhanced and data-driven approaches. Conventional summative scores tend to reduce multidimensional rubric-based tests to simple categories, which hides subtle trends in student performances. This paper aims to determine hidden performance student profiles through technology-enhanced data analytics. To achieve this, we applied unsupervised machine learning techniques, including Principal Component Analysis (PCA) for dimensionality reduction and two clustering methods (K-Means and Bisecting K-Means) to identify distinct student performance profiles. A total of 136 student records with ten rubric elements were evaluated with these unsupervised machine learning techniques. The results describe that there were two best student clusters suggested by internal measurement matrices, (Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index). Cluster 0 had consistently high balanced performance and scores across all elements, while Cluster 1 had students with uneven mastery. These results indicate that the PCA-Clustering approach is a powerful tool used to discover significant student portraits and promote more equitable, evidence-based assessment activities in the SDG 4 direction. Future work will include increasing dataset size and variation, and exploring adaptive AI-based feedback systems to support personalized and sustainable learning improvement.
Sammendrag
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