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All Journal International Journal of Advances in Applied Sciences
Asman, Saidatul Habsah
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Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Hybrid load forecasting considering energy efficiency and renewable energy using neural network Aizam, Adriana Haziqah Mohd; Dahlan, Nofri Yenita; Asman, Saidatul Habsah; Yusoff, Siti Hajar
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp759-768

Abstract

In recent years, the relationship between a country's gross domestic product (GDP) and its electricity consumption has changed significantly due to increased energy efficiency (EE) and renewable energy (RE) adoption. This decoupling disrupts conventional load forecasting models, affecting utility companies. This study has developed an innovative solution using an artificial neural network (ANN) Hybrid method for load forecasting, resulting in a remarkably accurate model with 99.68% precision. Applying this model to Malaysia's electricity consumption from 2020 to 2040 reveals a significant 13% reduction when accounting for EE and RE trends. This method aids risk management, contingency planning, and decision-making by accurately reflecting changing energy usage dynamics influenced by EE and RE sources.
Modelling soil deposition predictions on solar photovoltaic panels using ANN under Malaysia’s meteorological condition Suhaimi, Muhammad Aiman Amin Muhammad; Dahlan, Nofri Yenita; Asman, Saidatul Habsah; Rajasekar, Natarajan; Mohamed, Hassan
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp796-805

Abstract

Solar photovoltaic (PV) panels performance is influenced by various external factors such as precipitation, wind angle, ambient temperature, wind speed, transient irradiation, and soil deposition. Soiling accumulation on panels poses a significant challenge to PV power generation. This paper presents the development of an artificial neural network (ANN)-based soil deposition prediction model for PV systems. Conducted at a Malaysian solar farm over three months, the research utilized power output data from the inverter as model output and meteorological data as input variables. The model employed the Levenberg-Marquardt backpropagation method with Tansig and Purline activation functions. Performance assessment via statistical comparison of experimental and simulated results revealed a coefficient of determination (R2) value of 0.68073 for the ANN architecture of 5 input layers, 30 hidden layers, and 1 output layer (5-30-1). Sensitivity analysis highlighted relative humidity and wind direction as the most influential parameters affecting PV soiling rate. The developed ANN model, combined with sensitivity analysis, serves as a robust foundation for enhancing the efficiency of smart sensors in PV module cleaning systems.
Co-Authors Aizam, Adriana Haziqah Mohd Dahlan, Nofri Yenita Hassan Mohamed Rajasekar, Natarajan Suhaimi, Muhammad Aiman Amin Muhammad Yusoff, Siti Hajar