Short-Term National Power System Electricity Demand Forecasting using the MARSplines Method

被引：3

作者：

Czapaj, Rafal ^{[1
]}

Benalcazar, Pablo ^{[2
]}

Kaminski, Jacek ^{[2
]}

机构：

[1] PSE Innowacje Sp Zoo, Ctr Kompetencji Badania & Rozwoj, Ul Jordana 25, PL-40056 Katowice, Poland

[2] Polskiej Akad Nauk, Inst Gospodarki Surowcami Mineralnymi & Energia, Ul J Wybickiego 7A, PL-31261 Krakow, Poland

来源：

PRZEGLAD ELEKTROTECHNICZNY | 2019年 / 95卷 / 07期

关键词：

MARSplines Method; Explanatory Variable; Statistical Analysis; Forecasting; National Power System; Power Load;

D O I：

10.15199/48.2019.07.27

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The article presents the results obtained from applying the MARSplines method, which belongs to a broad group of Data Mining methods, to forecast the electric power demand in the Polish National Power System. Furthermore, the relationship between explanatory variables and the forecasted variable is examined through an extensive statistical analysis. Based on the (ex-post and ex-ante) simulations results a number of conclusions are drawn regarding the method itself and the accuracy of its predictions.

引用

页码：133 / 136

页数：4

共 11 条

[1]

[Anonymous], 1993, Technical Report

[2]

Friedman J. H., 1993, ESTIMATING FUNCTIONS

[3] MULTIVARIATE ADAPTIVE REGRESSION SPLINES [J].

FRIEDMAN, JH .

ANNALS OF STATISTICS, 1991, 19 (01) :1-67

[4]

Hastie T., 2001, The elements of statistical learning: data mining, inference, and predictionM

[5]

Lewicki P, STAT METHODS APPL, P403

[6]

Migut G., 2009, CZY STOSOWANIE METOD, P49

[7]

Nisbet N., 2009, HDB STAT ANAL DATA M, V83, P159

[8] Adaptation of regional digital soil mapping for precision agriculture [J].

Soderstrom, Mats ;

Sohlenius, Gustav ;

Rodhe, Lars ;

Piikki, Kristin .

PRECISION AGRICULTURE, 2016, 17 (05) :588-607

[9]

Sokolowski A, 2002, METODY STOSOWANE DAT, P5

[10]

Sokolowski A., 2004, DATA MINING PROGNOZO, P91

← 1 2 →