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In response to global environmental concerns and rising energy demands, there is great potential to use sustainable energy sources. Solar energy, particularly photovoltaic (PV) technology, appears as an environmentally beneficial alternative with untapped potential that outperforms current world energy demands. Off-grid solar PV systems are critical for attaining the Sustainable Development Goals (SDGs) since they provide underprivileged areas with decentralized and sustainable energy access while also addressing environmental and social objectives. Therefore, this investigation takes place in two parts. First, a 3.2 kW off-grid solar PV system was constructed and constructed on the roof of a home in Nahr al-Bared, Lebanon. Meteorological data and PV system output power were monitored with an ambient weather home station and multifunction solar equipment. The second step included developing artificial neural networks, ARIMA, and response surface methodology (RSM) models to predict the PV of the system’s hourly power output. Various statistical criteria were used to evaluate the model’s accuracy. The results show that the ARIMA model is the best for estimating PV output power. This study provides valuable insights into the feasibility and predictability of off-grid solar PV installations.

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Off-Grid Rooftop Solar PV Systems for Sustainable Household Energy in Nahr El-Bared, Lebanon: An Experimental Study and Empirical Model Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Youssef Kassem,
  • Hüseyin Çamur,
  • Osama Abed Al Halim

摘要

In response to global environmental concerns and rising energy demands, there is great potential to use sustainable energy sources. Solar energy, particularly photovoltaic (PV) technology, appears as an environmentally beneficial alternative with untapped potential that outperforms current world energy demands. Off-grid solar PV systems are critical for attaining the Sustainable Development Goals (SDGs) since they provide underprivileged areas with decentralized and sustainable energy access while also addressing environmental and social objectives. Therefore, this investigation takes place in two parts. First, a 3.2 kW off-grid solar PV system was constructed and constructed on the roof of a home in Nahr al-Bared, Lebanon. Meteorological data and PV system output power were monitored with an ambient weather home station and multifunction solar equipment. The second step included developing artificial neural networks, ARIMA, and response surface methodology (RSM) models to predict the PV of the system’s hourly power output. Various statistical criteria were used to evaluate the model’s accuracy. The results show that the ARIMA model is the best for estimating PV output power. This study provides valuable insights into the feasibility and predictability of off-grid solar PV installations.