Medium-term electric load forecasting using singular value decomposition

Abu-Shikhah, Nazih; Elkarmi, Fawwaz

doi:10.1016/j.energy.2011.04.017

Cited by 69 publications

(38 citation statements)

References 18 publications

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“…3 shows the correlation between interval-valued loads and interval-valued temperatures by using an interval-valued Scatter plot. 2 As can be seen from these two figures, the lower the temperature is, the more electricity demand is needed. That is, a negative correlation between the load demand and daily temperature across a year can be easily observed.…”

Section: Data Description and Analysismentioning

confidence: 91%

Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection

Bao

Chiong

et al. 2015

Energy

110

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Section: Data Description and Analysismentioning

confidence: 91%

Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection

Bao

Chiong

et al. 2015

Energy

110

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“…In literature, there are different methods for forecasting the future energy . In Li et al a method has been proposed for short‐term load forecasting based on wavelet transform and partial least squares regression.…”

Section: Introductionmentioning

confidence: 99%

“…In Li et al a method has been proposed for short‐term load forecasting based on wavelet transform and partial least squares regression. In another research, hourly loads and peak load for the next selected time span have been predicted using hourly loads of successive years . In the mentioned reference, hourly load is divided into three main components: a load trend‐following component, a random component, and a denoised component.…”

Section: Introductionmentioning

confidence: 99%

“…In literature, there are different methods for forecasting the future energy. [1][2][3][4][5][6][7][8][9] In Li et al 4 a method has been proposed for short-term load forecasting based on wavelet transform and partial least squares regression. In another research, hourly loads and peak load for the next selected time span have been predicted using hourly loads of successive years.…”

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confidence: 99%

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A new combined method for future energy forecasting in electrical networks

Baesmat

Shiri

2018

Int Trans Electr Energ Syst

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Summary Growing the industry and population in a region leads to the growth of required amount of electrical energy. Electrical companies should provide high quality energy according to the demand of customers. Load is an effective parameter of any system, and the network programmers should consider the annual load growth of the system and predict the required energy for each region by investigating previously recorded data of consumed electrical energy and stochastic analysis. In this paper, a new combined method for long‐term energy forecasting is proposed. This method, which combines the land‐consumption method and curve fitting based on generalization method, in addition to having simple calculations, takes into account the saturation. Moreover, loads from detailed formal program provided by the relevant institutes have been used, which results in better coordination between all organizations in charge of energy predictions and development of the countries. A suitable filtering method is also employed for input data to improve the method accuracy. To show the effectiveness of the proposed method, the results of different methods have been compared with those of the proposed method as well as real data. Then, real data of former 11 years of consumed energy gathered from Shiraz Electrical Distribution Company subscribers are employed and the energy for future 11 years is forecasted.

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“…With regard to the forecasting interval, different typologies can be identified: very short-term forecasting; short-term forecasting (STLF); and .3medium-term [6] and long-term forecasting [7]. Regarding the number of forecasting values, there are two main groups: a single value or more-than-one value.…”

Section: Related Workmentioning

confidence: 99%

Artificial neural networks for short-term load forecasting in microgrids environment

Hernández

Baladrón

Aguiar

et al. 2014

Energy

193

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The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption conditions. However, traditional forecasting has been performed only for extremely large areas, such as nations and regions. This work aims at presenting a solution for Short-Term Load Forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a Self-Organizing Map (SOM), a clustering of the previous partition via kmeans algorithm, and finally demand forecasting for each cluster with a MultiLayer Perceptron.Model validation was performed with data from a microgrid-sized environment provided by the Spanish company Iberdrola.

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Medium-term electric load forecasting using singular value decomposition

Abstract: ABSTRACT

Cited by 69 publications

References 18 publications

Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection

Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection

A new combined method for future energy forecasting in electrical networks

Artificial neural networks for short-term load forecasting in microgrids environment

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