An evaluation of conventional and computational intelligence methods for medium and long-term load forecasting in Algeria

Laouafi, Abderrezak; Mordjaoui, Mourad; Laouafi, Farida

doi:10.1109/ceit.2015.7233138

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…The lowest MAPE obtained by Laouafi et al was with the hybrid model adaptive neuro‐fuzzy inference system, resulting in an error of 3.017%, and techniques using ANN resulted in an error of only 5.925%, higher than that of ANNs using MLP‐based learning techniques.…”

Section: Simulation Results and Discussionmentioning

confidence: 83%

“…To reinforce a demonstration of system performance, the obtained MAPEs are compared with those of another more recent long‐term forecast article. Laouafi et al applied 4 load forecast models in the period from 2010 to 2014. The models used are Holts exponential smoothing technique, seasonal autoregressive integrated moving average, ANN (based on backpropagation training), and again a hybridized model with ANN + fuzzy.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

Miranda

Abaide

Sperandio

et al. 2018

Int Trans Electr Energ Syst

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Summary Over the past few decades, the behavior of electricity consumption has been changing, especially because of improvements in the distributed generation segment and technological innovations presented by smart grids. The use of microgeneration and the availability of electricity pricing in real time allow consumers to control their consumption, or generation, according to market conditions. This new dynamic tends to increasingly change the price elasticity of electricity demand, by indicating the need to readjust load forecasting models. In this market environment, in addition to providing robust estimates for the planning and operation of electric power systems, load forecasting models have become fundamental in the context of demand management. Thus, this paper proposes to develop an artificial neural network and fuzzy logic for load forecasting to perform an efficiency analysis. This system is able to provide estimates of the elasticity of electricity demand behavior with more satisfactory results. To do so, improvements in the neural network with multilayer perceptron are proposed. In this case, the adaptation of parameters to correlate variations in consumption with the changes in electricity tariffs was developed. The addition of this new structure produced better results compared with the conventional neural network. Computer tests were conducted using historical data from the ISO New England Inc and PJM Interconnection. Price elasticity estimates of electricity demand showed a sharp increase of demand in relation to the elasticity behavior.

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Section: Simulation Results and Discussionmentioning

confidence: 83%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

Miranda

Abaide

Sperandio

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…STLF is utilized from an hour-ahead to a day-ahead forecasting in power system operation. MTLF [6]- [9] and LTLF [9]- [11] are ranged respectively from one week to one year, and one year to decades. From the demand side's point of view, demand monitoring and forecasting have become a crucial part of power system in recent years.…”

Section: Introductionmentioning

confidence: 99%

ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand

Amini

Razban

et al. 2018

Energy

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This paper proposes an innovative algorithm for predicting short-term electrical maximum demand by using historical demand data. The ability to recognize in peak demand pattern for commercial or industrial customers would propose numerous direct and indirect benefits to the customers and utility providers in terms of demand reduction, cost control, and system stability. Prior works in electrical maximum demand forecasting have been mainly focused on seasonal effects, which is not a feasible approach for industrial manufacturing facilities in short-term load forecasting. The proposed algorithm, denoted as the Adaptive Rate of Change (ARC), determines the logarithmic rate-of-change in load profile prior to a peak by postulating the demand curve as a stochastic, mean-reverting process. The rationale behind this analysis, is that the energy efficient program requires not only demand estimation but also to warn the user of imminent maximum peak occurrence. This paper analyzes demand trend data and incorporates stochastic model and mean reverting half-life to develop an electrical maximum demand forecasting algorithm, which is statistically evaluated by cross-table and F-score for three different manufacturing facilities. The aggregate results show an overall accuracy of 0.91 and a F-score of 0.43, which indicates that the algorithm is effective predicting peak demand in predicting peak demand.

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“…Operators can rely on MTLF in making decisions for unit commitment, system security analysis, dispatching schedule and load flow analysis. Therefore, improving MTLF accuracy is crucial for increasing the efficiency of systems and reducing the costs [4].…”

Section: Introductionmentioning

confidence: 99%

An Accurate Medium-Term Load Forecasting based on Hybrid Technique

Yasin¹,

Aziz²,

Salim³

et al. 2018

IJEECS

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An accurate medium term load forecasting is significant for power generation scheduling, economic and reliable operation in power system. Most of classical approach for medium term load forecasting only consider total daily load demand. This approach may not provide accurate results since the load demand is fluctuated in a day. In this paper, a hybrid Ant-Lion Optimizer Least-square Support Vector Machine (ALO-LSSVM) is proposed to forecast 24-hour load demand for the next year. Ant-Lion Optimizer (ALO) is utilized to optimize the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimization is to minimize the Mean Absolute Percentage Error (MAPE). The performance of ALO-LSSVM technique was compared with those obtained from LS-SVM technique through a 10-fold cross-validation procedure. The historical hourly load data are analyzed and appropriate features are selected for the model. There are 24 inputs and 24 outputs vectors for this model which represents 24-hour load demand for whole year. The results revealed that the high accuracy of prediction could be achieved using ALO-LSSVM.

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An evaluation of conventional and computational intelligence methods for medium and long-term load forecasting in Algeria

Cited by 9 publications

References 23 publications

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand

An Accurate Medium-Term Load Forecasting based on Hybrid Technique

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