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Forecasting hydrogen production potential in islamabad from solar energy using water electrolysis
Authors:Syed Altan Haider  Muhammad Sajid  Saeed Iqbal
Affiliation:1. School of Mechanical & Manufacturing Engineering (SMME), National University of Sciences & Technology (NUST), Islamabad, Pakistan;2. US-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, Paksitan
Abstract:The focus of this study is the use of Machine Learning methods to forecast Solar Hydrogen production potential for the Islamabad region of Pakistan. For this purpose, we chose a Photovoltaic-Electrolytic (PV-E) system to forecast electricity and, hence, hydrogen production. The weather data used for forecasting and simulation were recorded with precise meteorological instruments stationed in Islamabad, over the course of 13 and a half months. Out of the three tested algorithms, Prophet performs the best with Mean Absolute Percentage Error of 3.7%, forecasting a daily average Hydrogen production of 93.3 × 103 kg/Km2. Although, the forecast in this study is made for the month of August and September, during which the local season moves towards winter, this study demonstrates solar hydrogen production, as a green energy source, has a tremendous potential in this region.
Keywords:Machine learning  Forecasting  Linear methods  Hydrogen production  Solar irradiance
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