Effective model for next day load curve forecasting based upon combination of perceptron and kohonen ANNs applied to iran power network

Farhadi, Mahdi; Moghaddas-Tafreshi, Seyed Masoud

doi:10.1109/intlec.2007.4448780

Cited by 4 publications

(12 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Farhadi and Tafreshi [27], SOM is used to classify normal and abnormal days, and a MultiLayer Perceptron (MLP) to manage temperature data. From a different perspective, Fan et al [28] use a particular method to forecast the price of electricity with SOM and SVM.…”

Section: Related Workmentioning

confidence: 99%

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

Hernández

Baladrón

Aguiar

et al. 2014

Energy

193

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Hernández

Baladrón

Aguiar

et al. 2014

Energy

193

View full text Add to dashboard Cite

show abstract

“…We propose efficient rules of Similar Sampling Process to train Kohonen neural network in the load forecasting models. According to these rules, each of the "normal week days", "official holidays", "before official holidays", "after official holidays" and "Ramadan days" have their own specific behaviors [13].…”

Section: Introductionmentioning

confidence: 99%

“…It is noteworthy that previous author's papers [13], [17] forecast the short-term (the next day) load, while the present paper focuses only on the method of classifying daily electric load and the classification of loads with the same behavior for the time period selected (e.g. one week, one month, or one year, or any desired time interval from one day to several years).…”

Section: Introductionmentioning

confidence: 99%

A self organizing map (SOM) based electric load classification

Farhadi

2018

FACTA U EE

View full text Add to dashboard Cite

It is of vital importance to use proper training data to perform accurate shortterm load forecasting (STLF) based on artificial neural networks. The pattern of the loads which are used for the training of Kohonen Self Organizing Map (SOM) neural network in STLF models should be of the highest similarity with the pattern of the electric load of the forecasting day. In this paper, an electric load classifier model is proposed which relies on the pattern recognition capability of SOM. The performance of the proposed electric load classifier method is evaluated by Iran electric grid data. The proposed method requires a very few number of training samples for training the Kohonen neural network of the STLF model and can accurately predict electric load in the network.

show abstract

“…References [1][2][3] build their load forecasting models based on temperature as primarily meteorological factor. However, temperature is only one of numerous principal factors intensely impacting load characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…When day type of the i th historical load and predicted load is the same and different, Preprocess of Input LoadFirstly, recent historical load with least relevancy is cancelled. The rest of data is then smoothed by Minimum Mean Square Error Filter 1. N recent historical loads within one to three weeks before predicted load are selected.…”

mentioning

confidence: 99%

A Power Load Forecasting Method Based on Matching Coefficient of Meteorological Factors and Similar Load Modification

Zheng

Chen

Fan

2012

2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics

View full text Add to dashboard Cite

It is of great significance to enhance the precision of power load forecasting for the security and stability of power system. According to the power load characteristics of both regularity and randomness, a novel load forecasting method based on the integration of similar load and recent historical load is proposed. In terms of similar load, this algorithm, firstly, utilizes matching coefficient of meteorological factors to quantify the similarity, then uses membership of day-type for the modification of similarity. After the process of filtering, the input sequence is made up of the combination of modified similar load and recent historical load. The results of simulation indicate that, compared with these forecasting algorithms merely based on similar load or on recent historical load, both mean relative error and mean square deviation of the algorithm presented in this paper are smaller and the precision of load prediction is higher.

show abstract

Effective model for next day load curve forecasting based upon combination of perceptron and kohonen ANNs applied to iran power network

Cited by 4 publications

References 10 publications

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

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

A self organizing map (SOM) based electric load classification

A Power Load Forecasting Method Based on Matching Coefficient of Meteorological Factors and Similar Load Modification

Contact Info

Product

Resources

About