Impact of temperature on price and load.

<div><p>Accurate forecasting of electricity prices and loads is challenging in smart grids due to the strong interdependence between load and price. To address this, we propose two deep recurrent neural network models that forecast both load and price concurrently. The first model, Spars...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Nasir Nauman (22272299) (author)
مؤلفون آخرون:	Sheeraz Akram (17251060) (author), Muhammad Rashid (2836613) (author), Arfan Jaffar (19105558) (author), Sohail Masood Bhatti (19105561) (author), Benish Fida (22272302) (author)
منشور في:	2025
الموضوعات:	Medicine Sociology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified lower error rates enhanced feature extraction 57 %&# 8722 2385s vs 3100s 21 %, rmse improve forecasting accuracy europe ), iex sparse encoder nar smart grids due outperformed benchmark models lstm reached rmse integrates long short 74 mw ), senarx ), introduces term load forecasting iex ), sparse encoder smart grids india ), term memory time forecasting concurrent forecasting 33 mw 02 mw price forecasting uncertain markets traditional methods strong interdependence senarx achieved paper presents nonlinear autoregression models suitable model approach japan ). genetic algorithm future work first model exogenous inputs electricity prices data noise comparative study characteristics make based optimization 40 %.
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Impact of temperature on price and load.

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