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Short-Term Electricity Demand Forecasting with Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Short-Term Electricity Demand Forecasting with Machine Learning./
作者:	Madrid, Ernesto Javier Aguilar.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	53 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Software. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28992443
ISBN:	9798209806882

Short-Term Electricity Demand Forecasting with Machine Learning.
Madrid, Ernesto Javier Aguilar.

Short-Term Electricity Demand Forecasting with Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 53 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Master's)--Universidade NOVA de Lisboa (Portugal), 2021.

This item must not be sold to any third party vendors.

An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units' planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydro-thermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This project proposes a set of machine learning (ML) models to improve the accuracy of 168 hours forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model's interpretation, which provided a relevant additional result, the features' importance in the forecasting.

ISBN: 9798209806882Subjects--Topical Terms:

619355
Software.

Short-Term Electricity Demand Forecasting with Machine Learning.
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