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Khaled Sedraoui

Researcher at King Abdulaziz University

Publications -  34
Citations -  1217

Khaled Sedraoui is an academic researcher from King Abdulaziz University. The author has contributed to research in topics: Computer science & Photovoltaic system. The author has an hindex of 9, co-authored 23 publications receiving 545 citations.

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Optimal Load Scheduling of Plug-In Hybrid Electric Vehicles via Weight-Aggregation Multi-Objective Evolutionary Algorithms

TL;DR: This paper formulates a multi-objective load scheduling problem to minimize two competing objectives: 1) potential serious peak-to-valley difference and 2) economic loss and proposes a weight aggregation (WA) strategy and implements a novel MOEA algorithm named WA-MOPSO by incorporating WA into MOPSO to solve the problem.
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Optimal sizing of grid-connected photovoltaic energy system in Saudi Arabia

TL;DR: In this paper, the optimal array and inverter size for a grid-connected PV system was analyzed for serving electricity in Makkah, Saudi Arabia, with an optimal inverter ratio of R = 1 with minimized CO2 emissions.
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PV array power output maximization under partial shading using new shifted PV array arrangements

TL;DR: In this paper, three new physical PV array arrangements are proposed to mitigate partial shading effects, based on maximizing the distance between adjacent PV modules within a PV array by appropriately arranging modules in different rows and columns without changing the electrical connections.
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Disassembly Sequence Planning: A Survey

TL;DR: This paper discusses the recent progress of disassembly sequencing planning in four major aspects: product disassembly modeling methods, mathematical programming methods, artificial intelligence methods, and uncertainty handling.
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On the investigation of photovoltaic output power reduction due to dust accumulation and weather conditions

TL;DR: In this paper, the effects of dust accumulation and weather conditions on PV panel power output were analyzed after one to four weeks of exposure, and the results revealed that local environmental conditions, i.e., dust, rain, and partial cloud, significantly reduce PV power output.