Prognozowanie krótkoterminowe zapotrzebowania na energię elektryczną w KSE z wykorzystaniem metody MARSplines

Czapaj, Rafał; Benalcazar, Pablo; Kamiński, Jacek

doi:10.15199/48.2019.07.27

Cited by 2 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Z przeglądu literaturowego związanego z krótkoterminowym prognozowaniem zużycia energii wynika, że autorzy opracowań, publikacji i wdrożonych aplikacji stosowali różne rozwiązania badanego problemu: • sieci neuronowe (Cieślak, 1996;Kelner, 2003;Wójcik, 2005;Maciejasz, 2006;Kogut, 2007;Jeżyk i Tomczewski, 2014;Szoplik, 2015;Cieślik i Kogut, 2016;Zheng et al, 2017); • adaptacyjne metody regresyjne (Sokołowski i Pasztyła, 2004;Czapaj et al, 2019); • algorytmy genetyczne (Yu i Xu, 2014); • liniowe modele ekonometryczne (Łucki, 1988;Zieliński, 1995;Gawlik, 2008;Łucki et al, 2008;Bianco et al, 2014aBianco et al, , 2014b. Większość opracowań oparta jest na algorytmach należących do szeroko pojmowanej grupy metod sztucznej inteligencji, w szczególności sieci neuronowych.…”

Section: Wybór Modelu I Podstawowe Założeniaunclassified

Ekonometryczny model krótkoterminowego prognozowania zużycia gazu

Kwilosz¹,

Badawczy²,

Filar³

2021

View full text Add to dashboard Cite

In order to develop a mathematical model of short-term gas demand, it is necessary to analyze the latest mathematical forecasting methods in order to select and adapt the right one (meeting the condition of efficiency and effectiveness). It is necessary to recognize and analyze factors (mainly environmental) affecting the result of short-term forecasts and sources of data that can be used. The result of the work is a numerical model of short-term gas demand for a selected territorial unit of the country. The developed model was calibrated and tested on historical data describing environmental conditions and real gas consumption. A heterogeneous linear econometric model was designed and calibrated on the basis of a selected set of attributes (explanatory variables). The estimated parameters of the model were statistically verified. It is worth noting that in the short term of the forecast (7 days) there are no significant changes in the gas market environment (launching new investments, connecting new users to the system, or changes in demand resulting from changing macroeconomic conditions). Other technical factors, such as production line failures at customers or industrial downtime, are difficult to predict, or knowledge about their occurrence is rarely available. For this reason, the only factors that may have an impact on changes in gas demand in the short term are weather factors, which were selected as explanatory variables for the developed model. Historical weather data was retrieved from the OpenWeatherMapHistoryBulk web service. Daily values of gas consumption for one of the voivodships of southern Poland were used as the response variable. The data was downloaded from the information exchange system of the transmission pipeline operator. The data covers a three-year period, as only such data has been made public. The explanatory variables include the daily values of weather data such as: average temperature, chilled temperature, minimum temperature, maximum temperature, atmospheric pressure, relative humidity, wind speed and wind direction.

show abstract

Section: Wybór Modelu I Podstawowe Założeniaunclassified

Ekonometryczny model krótkoterminowego prognozowania zużycia gazu

Kwilosz¹,

Badawczy²,

Filar³

2021

View full text Add to dashboard Cite

show abstract

A Review of Auto-Regressive Methods Applications to Short-Term Demand Forecasting in Power Systems

Czapaj¹,

Kamiński

Sołtysik

2022

Energies

View full text Add to dashboard Cite

The paper conducts a literature review of applications of autoregressive methods to short-term forecasting of power demand. This need is dictated by the advancement of modern forecasting methods and their achievement in good forecasting efficiency in particular. The annual effectiveness of forecasting power demand for the Polish National Power Grid for the next day is approx. 1%; therefore, the main objective of the review is to verify whether it is possible to improve efficiency while maintaining the minimum financial outlays and time-consuming efforts. The methods that fulfil these conditions are autoregressive methods; therefore, the paper focuses on autoregressive methods, which are less time-consuming and, as a result, cheaper in development and applications. The prepared review ranks the forecasting models in terms of the forecasting effectiveness achieved in the literature on the subject, which enables the selection of models that may improve the currently achieved effectiveness of the transmission system operator. Due to the applied approach, a transparent set of forecasting methods and models was obtained, in addition to knowledge about their potential in the context of the needs for short-term forecasting of electricity demand in the national power system. The articles in which the MAPE error was used to assess the quality of short-term forecasts were analyzed. The investigation included 47 articles, several dozen forecasting methods, and 264 forecasting models. The articles date from 1997 and, apart from the autoregressive methods, also include the methods and models that use explanatory variables (non-autoregressive ones). The input data used come from the period 1998–2014. The analysis included 25 power systems located on four continents (Asia, Europe, North America, and Australia) that were published by 44 different research teams. The results of the review show that in the autoregressive methods applied to forecasting short-term power demand, there is a potential to improve forecasting effectiveness in power systems. The most promising prognostic models using the autoregressive approach, based on the review, include Fuzzy Logic, Artificial Neural Networks, Wavelet Artificial Neural Networks, Adaptive Neurofuse Inference Systems, Genetic Algorithms, Fuzzy Regression, and Data Envelope Analysis. These methods make it possible to achieve the efficiency of short-term forecasting of electricity demand with hourly resolution at the level below 1%, which confirms the assumption made by the authors about the potential of autoregressive methods. Other forecasting models, the effectiveness of which is high, may also prove useful in forecasting by electricity system operators. The paper also discusses the classical methods of Artificial Intelligence, Data Mining, Big Data, and the state of research in short-term power demand forecasting in power systems using autoregressive and non-autoregressive methods and models.

show abstract

Prognozowanie krótkoterminowe zapotrzebowania na energię elektryczną w KSE z wykorzystaniem metody MARSplines

Cited by 2 publications

References 2 publications

Ekonometryczny model krótkoterminowego prognozowania zużycia gazu

Ekonometryczny model krótkoterminowego prognozowania zużycia gazu

A Review of Auto-Regressive Methods Applications to Short-Term Demand Forecasting in Power Systems

Contact Info

Product

Resources

About