Fuzzy short-term electric load forecasting

Al-Kandari, A.M.; Soliman, S. A.; El-Hawary, M.E.

doi:10.1016/s0142-0615(03)00069-3

Cited by 79 publications

(22 citation statements)

References 32 publications

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“…Many techniques have been proposed in recent decades for STLF such as statistical models [49][50][51], fuzzy methods [52][53][54] and machine learning algorithms [55][56][57][58].…”

Section: Introductionmentioning

confidence: 99%

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Sheikhan

Mohammadi

2012

Neural Comput & Applic

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Artificial neural networks have been widely used in time series prediction. In this paper, multi-layer feedforward neural networks with optimized structures, using particle swarm optimization (PSO) algorithm, are used for hourly load prediction based on load time series of IEEE Reliability Test System. To have a small and appropriate feature subset, a hybrid method is used for feature selection in this paper. This hybrid method uses the combination of genetic algorithm (GA) and ant colony optimization (ACO) algorithm. The season, day of the week, time of the day and history load are considered as load influencing factors in this study based on the mentioned standard load dataset. The optimized number of neurons in the hidden layers of multi-layer perceptron (MLP) is determined using PSO algorithm. Experimental results show that the proposed hybrid model offers superior performance, in terms of mean absolute percentage error (MAPE), in time series prediction as compared to some recent researches in this field.

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“…Many techniques have been proposed in recent decades for STLF such as statistical models [49][50][51], fuzzy methods [52][53][54] and machine learning algorithms [55][56][57][58].…”

Section: Introductionmentioning

confidence: 99%

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Sheikhan

Mohammadi

2012

Neural Comput & Applic

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“…Two variables zl,1 and zl,2 stand for two random variables location of zl which is defined in (11). Also, the standard location of the random variables zl ( ζl,k ) is selected as follow:…”

Section: M Pem Stochastic Analysismentioning

confidence: 99%

“…In [10], fuzzy logic theory idea is used to investigate the optimal capacitor allocation problem. Recently, fuzzy set theory has caught the attraction of researcher for designing intelligent systems in broad range of applications such as electrical load forecasting [11], designing fuzzy controller for industrial robots [12,13], powerful tool for system uncertainty compensator and optimization method with high rate of accuracy and performance [14,15], adaptive model for non-linear controllers [16,17], etc. Here the system is modeled by using fuzzy membership function which most challenging issue would be the proper choose of membership functions and identify corresponding rule properly and precisely.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Reactive Power Compensation Using Capacitor Allocation Based on Modified Harmony Search Algorithm

Wáng¹,

Cheong²,

Chén³

2017

IJCATR

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This paper presents a heuristics based approach for capacitor allocation problem incorporating Point Estimate Method (PEM) based analysis to attain optimal capacitor reallocation in radial distribution systems. The proposed probabilistic method is used to achieve optimal capacitor allocation associated with the active/reactive loads forecasted error as well as energy loss reduction by taking into account the cost function coefficients. This problem is formulated as a mix integer non-linear, discrete multi-objective problem which be in need of a robust optimization tool to avoid trapping in local optima. The proposed solution based on modified harmony search (MHS) algorithm employ to search for a set of Pareto optimal value of objective functions which save in memory that called repository. Moreover, a fuzzy clustering method is utilized to control the size memory (repository) for the concerned problem.The proposed approach has been tested on IEEE 9 buses to check feasibility and robustness of the proposed approach.

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“…The techniques are mostly based on statistical and time-series analysis [1], learning algorithms, or expert systems [2]. Neural networks (NNs) [3][4][5][6][7], fuzzy expert systems [8][9][10], wavelet-based networks [11][12][13], or a combination of these methods [14][15][16][17] are examples of expert systems that have been investigated for short-term prediction in the literature.…”

Section: Introductionmentioning

confidence: 99%

Short-term load forecasting using mixed lazy learning method

Barakati¹,

Gharaveisi²,

Rafiei³

2015

Turk J Elec Eng & Comp Sci

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Abstract:A novel short-term load forecasting method based on the lazy learning (LL) algorithm is proposed. The LL algorithm's input data are electrical load information, daily electricity consumption patterns, and temperatures in a specified region. In order to verify the ability of the proposed method, a load forecasting problem, using the Pennsylvania-New Jersey-Maryland Interconnection electrical load data, is carried out. Three LL models are proposed: constant, linear, and mixed models. First, the performances of the 3 developed models are compared using the root mean square error technique. The best technique is then selected to compete with the state-of-the-art neural network (NN) load forecasting models. A comparison is made between the performances of the proposed mixed-model LL as the superior LL model and the radial basis function and multilayer perceptron NN models. The results reveal significant improvements in the precision and efficiency of the proposed forecasting model when compared with the NN techniques.

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Fuzzy short-term electric load forecasting

Cited by 79 publications

References 32 publications

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Stochastic Reactive Power Compensation Using Capacitor Allocation Based on Modified Harmony Search Algorithm

Short-term load forecasting using mixed lazy learning method

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