article · Energy Reports
Electric energy demand forecasting is vital for modern power systems, particularly alongside market deregulation and the expanding role of industrial consumers. Standard forecasting techniques frequently face drawbacks such as slow convergence and high computational complexity. To tackle these challenges, a hybrid model pairs the Adaptive Neuro-Fuzzy Inference System with Gene Expression Programming to improve predictions of electrical energy consumption. The methodology was validated using real-time monthly electrical load measurements from an industrial consumer located in Uganda. When tested against standalone versions of both underlying tools, the integrated model delivered superior predictive capabilities, achieving reduced error levels and minimal computation time. The resulting technique offers improved precision and operational efficiency for long-term load forecasting.
Reliable electricity demand forecasting is essential for stable grid management and long-term infrastructure planning. As electricity markets undergo deregulation and major industrial consumers shift demand patterns, improved prediction methods help grid operators and industrial facilities avoid costly operational inefficiencies by balancing power supply with demand accurately and with lower computational burden.
This applied and tested model is directly relevant to power utilities, energy planners, and large industrial facilities managing high power consumption. By reducing computational runtimes and prediction errors on real industrial load data from Uganda, the method holds potential for incorporation into commercial grid-management and energy-forecasting software. However, the abstract does not indicate whether it has been tested beyond a single industrial site or packaged into an automated commercial platform.
AI-generated from the published abstract. Always read the original work before citing.
Electric energy demand forecasting is vital in contemporary power systems, especially amidst market deregulation trends and the increasing influence of industrial customers on power dynamics. However, existing forecasting models encounter challenges such as slow convergence and high complexity. Addressing these issues, this study proposes a hybrid forecasting model that combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gene Expression Programming (GEP) to enhance predictions of electrical energy consumption. Validated using real-time monthly electrical load data from an industrial user in Uganda, the hybrid model outperforms individual ANFIS and GEP models, demonstrating reduced errors and minimal computation time. The application of this hybrid model presents promising results, showcasing exceptional predictive capabilities and offering potential improvements in efficiency and precision for electrical energy consumption forecasting amidst market deregulation and evolving industrial dynamics.
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DOI: 10.1016/j.egyr.2024.05.045
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