Short term electricity price forecast based on environmentally adapted generalized neuron

Singh, Nitin; Mohanty, Soumya R.; Shukla, Rishabh Dev

doi:10.1016/j.energy.2017.02.094

Cited by 86 publications

(30 citation statements)

References 48 publications

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“…A closer look at Figures 7,9,11,14,and 15 indicates that the hybrid model of MMPF (EKF-ARMA)+SVM performs much better than the rest since it has the lowest percentage error. The actual reason is because although the MMPF is indeed adaptive has a main disadvantage which is that in its initial structure is not able to handle non-linearities and seasonalities.…”

Section: Discussionmentioning

confidence: 99%

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Application and Comparison of Evolutionary Techniques for Forecasting the Hellenic Grid Electricity Load

Pappas¹

2017

IJPER

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Abstract. Electric load forecasting is a process that has to be both fast and reliable. An accurate method of load forecasting plays the most crucial role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and operational problems connected to electrical energy production and distribution. In this study real data is used and the performance of three different techniques for adaptive electric load forecasting is evaluated. The first method is a combination of the well-established multimodel partitioning filter (MMPF) implementing extended Kalman filters (EKF) with Support Vector Machines (SVM), the second is the adaptive MMPF Kalman filters (KF) model and the third one is an artificial three layer feed-forward neural network (ANN). The results indicate that all three methods are reliable, however the combination of MMPF and SVM provides a more accurate load forecasting and at the same time identifies successfully both normal periodic behavior and any unusual activity of the electric grid.Keywords: Adaptive multimodel partitioning filter (MMPF), Support vector machines (SVM), artificial neural networks (ANN), forecasting, Kalman filters (KF), electricity demand load. IntroductionElectrical energy is considered as one of the most important factors that are closely related to both economic and social development. Accurate and fast load forecasting is related to a number of operational functions, such as system safety, power system real-time control, economic planning, grid's maintenance and also fuel planning. The problem of load forecasting has been studied extensively during recent decades. Some of the proposed techniques make use of time series analysis using ARMA [1][2][3][4][5] or ARIMA models [6][7][8][9][10]. Other algorithms achieve load forecasting by adopting evolutionary techniques such as ANN's [11-12], SVM's [13][14] either alone or combined with other methods for the same purpose [15][16][17].The purpose of this study is not to introduce one more load forecasting criterion, but it focuses on applying three different methods to real electric load data and evaluating their performance.The first method is based on a hybrid model that combines the adaptive MMPF [18][19][20], known for its stability, with SVM. The idea of using this method for electric load forecasting came from the fact that it was applied for wind speed prediction with very good results [21]. The data used now is not subjected to any prior -offline manipulation in order to remove weekly and annual seasonality as was done in previous cases [5]. That's why in this case the MMPF implements a bank of extended Kalman filters (EKF) with ARMA models instead of simple Kalman filters (KF) with ARMA models in order to handle data's non-linearities. MMPF with EKF combined with genetic algorithms (GA) were successfully applied in prediction of epilepsy and in the evolution of stock values using biomedical and financial data respectively [22].The second method, presented analytically in [5], impl...

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Section: Discussionmentioning

confidence: 99%

“…Some of the proposed techniques make use of time series analysis using ARMA [1][2][3][4][5] or ARIMA models [6][7][8][9][10]. Other algorithms achieve load forecasting by adopting evolutionary techniques such as ANN's [11][12], SVM's [13][14] either alone or combined with other methods for the same purpose [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Application and Comparison of Evolutionary Techniques for Forecasting the Hellenic Grid Electricity Load

Pappas¹

2017

IJPER

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“…Keles et al [23] develop a model based on ANNs and optimal parameter model. Singh et al [24] present a combined model with generalized neuron model and wavelet transform. Itaba and Mori [25] utilize the general radial basis function network and fuzzy clustering.…”

Section: Introductionmentioning

confidence: 99%

An accurate analysis of the parameters affecting consumption and price fluctuations of Electricity in the Iranian market during Summer

et al. 2020

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In this paper, a novel method is proposed to predict the cost of short-term hourly electrical energy based on combined neural networks. In this method, the influential parameters that play a key role in the accuracy of these systems are identified and the most prominent ones are selected. In the proposed method, initially, using the SOM network, similar days are placed in close clusters. In the next stage, the temperature parameter and prices pertaining to similar days are trained separately in two MLP neural networks because of their differences concerning the range of changes and their nature. Finally, the two networks are merged with another MLP network. In the proposed hybrid method, an evolutionary search method is used to provide an appropriate initial weight for neural network training. Given the price data changes, the price amidst the previous hour has a significant effect on the prediction of the current state. In this vein, in the proposed method, the predicted data in the previous hour is considered as one of the inputs of the next stage. The proposed method was assessed on the datasets of Iran in the summer. This information pertains to the 2011-2016 period.

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“…Each component is then appropriately transformed, normalised, and fed with time and date indices to a neural network, so that the features of individual components are properly captured. Singh et al (2017) provided a method in order to overcome the limitations of the classical ANN model, generalised neuron model is used for forecasting the short-term electricity price of Australian electricity market. The pre-processing of the input parameters is accomplished using wavelet transform for better representation of the low and high frequency components.…”

Section: Introductionmentioning

confidence: 99%

A new short-term energy price forecasting method based on wavelet neural network

Keynia

Heydari

2019

IJMOR

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A wavelet neural network (WNN) is proposed for short-term price forecasting (STPF) in electricity markets. Back propagation algorithm is used for training the wavelet neural network for prediction. Weights in the back propagation algorithm are usually initialised with small random values. If the random initial weights happen to be far from a suitable solution or near a poor local optimum, training may take a long time or get trapped in the local optimum. In this paper, we show that WNN has acceptable prediction properties compared to other forecasting techniques. We investigated proper weight initialisations of WNN, and proved that it attains a superior prediction performance. Finally, we used a two-step correlation analysis algorithm for input selecting. This algorithm selects the best relevant and non-redundant input features for WNN. Our model is examined for MCP prediction of the Spanish market and LMP forecasting in PJM (Pennsylvania, New Jersey and Maryland) market for the year 2002 and 2006 respectively.

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Short term electricity price forecast based on environmentally adapted generalized neuron

Cited by 86 publications

References 48 publications

Application and Comparison of Evolutionary Techniques for Forecasting the Hellenic Grid Electricity Load

Application and Comparison of Evolutionary Techniques for Forecasting the Hellenic Grid Electricity Load

An accurate analysis of the parameters affecting consumption and price fluctuations of Electricity in the Iranian market during Summer

A new short-term energy price forecasting method based on wavelet neural network

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