Výsledky vyhledávání - "Medium-term load forecasting"

Akademický článek

Analysis and Forecasting of Monthly Electricity Demand Time Series Using Pattern-Based Statistical Methods

Autor: Paweł Pełka

Publikováno v: Energies, Vol 16, Iss 2, p 827 (2023)

This article provides a solution based on statistical methods (ARIMA, ETS, and Prophet) to predict monthly power demand, which approximates the relationship between historical and future demand patterns. The energy demand time series shows seasonal f

Externí odkaz: https://doaj.org/article/ce75e9ac00624e42be36ee47c5a3875e

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Dissertation/ Thesis

Medium term load forecasting in South Africa using Generalized Additive models with tensor product interactions

Autor: Ravele, Thakhani

MSc (Statistics)
Department of Statistics
Forecasting of electricity peak demand levels is important for decision makers in Eskom. The overall objective of this study was to develop medium term load forecasting models which will help decisi

Externí odkaz: http://hdl.handle.net/11602/1165

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Akademický článek

Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes

Autor: Omaji Samuel, Fahad A. Alzahrani, Raja Jalees Ul Hussen Khan, Hassan Farooq, Muhammad Shafiq, Muhammad Khalil Afzal, Nadeem Javaid

Publikováno v: Entropy, Vol 22, Iss 1, p 68 (2020)

Over the last decades, load forecasting is used by power companies to balance energy demand and supply. Among the several load forecasting methods, medium-term load forecasting is necessary for grid’s maintenance planning, settings of electricity p

Externí odkaz: https://doaj.org/article/38ee36a5432b4778bfc047bac894e7a5

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Comparison of robust machine-learning and deep-learning models for midterm electrical load forecasting

Autor: FATİH BAL, Fatma Yaprakdal

Publikováno v: Volume: 12, Issue: 2 102-107
European Journal of Technique (EJT)
European Journal of Technique

Electrical load forecasting (ELF) is gaining importance especially due to the severe impact of climate change on electrical energy usage and dynamically evolving smart grid technologies in the last decades. In this regard, medium-term load forecastin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::272c3c2a996e725f2c9ac384e9bd528b
https://doi.org/10.36222/ejt.1201977

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Akademický článek

Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting

Autor: Salah Bouktif, Ali Fiaz, Ali Ouni, Mohamed Adel Serhani

Publikováno v: Energies, Vol 12, Iss 1, p 149 (2019)

Time series analysis using long short term memory (LSTM) deep learning is a very attractive strategy to achieve accurate electric load forecasting. Although it outperforms most machine learning approaches, the LSTM forecasting model still reveals a l

Externí odkaz: https://doaj.org/article/713d4388c99b4285973b0e37af7c44f9

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Akademický článek

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Akademický článek

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

Autor: Salah Bouktif, Ali Fiaz, Ali Ouni, Mohamed Adel Serhani

Publikováno v: Energies, Vol 11, Iss 7, p 1636 (2018)

Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and select

Externí odkaz: https://doaj.org/article/ad514c7fb4344cfc9bf90f8ddf302d07

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