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Dergiler / Istanbul University Journal of Electrical and Electronics Engineering / 2011 / Cilt: 11 - Sayı: 1

A novel hybrıd approach to short term load forecastıng

Ashish Kumar SRIVASTAVA
Tariqu ISLAM
Sayfa
1345–1354
Yayın Tarihi
2022-10-18
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The model is trained and tested on load and weather data pertaining to Norwalk/Stamford in Connecticut Valley Electric Exchange .

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The knowledge of a day ahead load is necessary for a utility in a competitive electricity market for fuel purchase scheduling, planning for energy transactions and to maintain their power reserve close to the minimum as required by Independent System Operator.
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Temel Bulgular & Veri:Among the four input structures, structure utilizing Neuro-Fuzzy approach with wind direction as one of the input parameters gives impressive result, with an average error of 1.735 %.
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Sonuç & Katkı:The model is trained and tested on load and weather data pertaining to Norwalk/Stamford in Connecticut Valley Electric Exchange .
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

The knowledge of a day ahead load is necessary for a utility in a competitive electricity market for fuel purchase scheduling, planning for energy transactions and to maintain their power reserve close to the minimum as required by Independent System Operator. Previous researches do not consider the effect of wind direction on load forecasting, however this paper investigates the effect of wind direction and weather event on load requirements and accordingly presents a novel Neuro-Fuzzy based approach to Short term load forecast (STLF) i.e. a day ahead average load forecast utilizing parameters identified as historical load, temperature, weather event (for e.g. fog and snow) and wind direction. Four different input structures, three using Neuro-Fuzzy approach and one using only Neural network (NN) are tested. Among the four input structures, structure utilizing Neuro-Fuzzy approach with wind direction as one of the input parameters gives impressive result, with an average error of 1.735 %. The model is trained and tested on load and weather data pertaining to Norwalk/Stamford in Connecticut Valley Electric Exchange .

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Bu Makaleye Atıf Yap

SRIVASTAVA, A. K., ISLAM, T. (2022). A novel hybrıd approach to short term load forecastıng. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 11 - Sayı: 1, 1345–1354. https://ajindex.com/articles/543703

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Kaynakça (13)

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