Hong Xian Li | Searcharxiv

arXiv2025

Assessment of a Hybrid Energy System for Reliable and Sustainable Power Supply to Boru Meda Hospital in Ethiopia

This study aims to evaluate the techno-economic feasibility of hybrid energy systems (HES) including Grid for providing reliable and sustainable power to Boru Meda Hospital, Ethiopia. HOMER pro 3.11.2 was used to design and evaluate a novel, integrated optimization and comparative assessment of diverse HRES, specif ically adjusted to the energy consumptions and available resources of the Hospital. The scenario evaluation showed that interconnecting photovoltaic (PV), biomass generator (BG), wind power (WP), diesel generator (DG), battery, and converter can effectively provide the Hospital's daily energy consumption of 11,214.66 kWh while conforming reliability and reducing emissions. The PV/BG/batt/conv configuration emerged as the most cost-effective and sustainable alternative, attaining the lowest LCOE of \$0.339/kWh, an NPC of \$25.7 million, and a 100% renewable energy fraction with simple pay back of 7.26 yr. As a result, the operational cost associated with the consumption of 500.00 L of diesel per month can be entirely avoided. The DG-integrated hybrids exhibit advanced techno-economic capability with significant worth, strong ROI (20\%) and IRR (18\%), endorsed by fast capital recovery (7.21-8.71 years). Overall, the hybrid system offers an optimal balance of cost, reliability, and sustainability, making it a promising and scalable solution for electrification of energy scare institution and areas in Ethiopia, thereby contributing to national sustainable energy development goals.

eess.SY↗

arXiv2024

Detecting Change Intervals with Isolation Distributional Kernel

Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have been proposed recently to identify those changes, they still suffer from missing subtle changes, poor scalability, or/and sensitivity to outliers. To meet these challenges, we are the first to generalise the CPD problem as a special case of the Change-Interval Detection (CID) problem. Then we propose a CID method, named iCID, based on a recent Isolation Distributional Kernel (IDK). iCID identifies the change interval if there is a high dissimilarity score between two non-homogeneous temporal adjacent intervals. The data-dependent property and finite feature map of IDK enabled iCID to efficiently identify various types of change-points in data streams with the tolerance of outliers. Moreover, the proposed online and offline versions of iCID have the ability to optimise key parameter settings. The effectiveness and efficiency of iCID have been systematically verified on both synthetic and real-world datasets.

cs.LG↗