Work

Hybrid load forecasting considering energy efficiency and renewable energy using neural network

Year: 2024

Type: article

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.

Source: International Journal of Advances in Applied Sciences

DOI: 10.11591/ijaas.v13.i4.pp759-768

Authors: Adriana Haziqah Mohd Aizam, Nofri Yenita Dahlan, Saidatul Habsah Asman, Siti Hajar Yusoff

Institutions: Universiti Teknologi MARA System, Universiti Teknologi MARA, International Islamic University Malaysia

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: diamond

OpenAlex ID: W4403509958

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