Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

Chitsaz, Hamed; Amjady, Nima; Zareipour, Hamidreza

doi:10.1016/j.enconman.2014.10.001

Cited by 210 publications

(69 citation statements)

References 42 publications

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“…In the case of ANN, various algorithms are explored [15,16]. In the presented case study, the Bayesian Regularization [17], Scalar Conjugate Gradient [18] and Levenberg-Marquardt [19] algorithms are explored.…”

Section: State Of the Artmentioning

confidence: 99%

A Two-step Forecasting Solution and Upscaling Technique for Small Size Wind Farms Located in Hilly Areas of Romania

Lungu¹,

Căruţaşu²,

Pîrjan³

et al. 2016

SIC

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Section: State Of the Artmentioning

confidence: 99%

A Two-step Forecasting Solution and Upscaling Technique for Small Size Wind Farms Located in Hilly Areas of Romania

Lungu¹,

Căruţaşu²,

Pîrjan³

et al. 2016

SIC

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“…Also a consistent approach is given by the use of ANNs for short-term generation forecast in case of wind turbines and photovoltaic (PV) panels. Various ANN-based algorithms are described in [27,28], it is proposed Bayesian Regularization algorithms for forecasting. Also in [29,30], authors proposed back propagation neural networks based on the optimization of Swarm particles.…”

Section: Consumption and Micro-generation Short-term Forecastingmentioning

confidence: 99%

Electricity Consumption and Generation Forecasting with Artificial Neural Networks

Bârã¹,

Oprea²

2018

Advanced Applications for Artificial Neural Networks

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Nowadays, smart meters, sensors and advanced electricity tariff mechanisms such as timeof-use tariff (ToUT), critical peak pricing tariff and real time tariff enable the electricity consumption optimization for residential consumers. Therefore, consumers will play an active role by shifting their peak consumption and change dynamically their behavior by scheduling home appliances, invest in small generation or storage devices (such as small wind turbines, photovoltaic (PV) panels and electrical vehicles). Thus, the current load profile curves for household consumers will become obsolete and electricity suppliers will require dynamical load profiles calculation and new advanced methods for consumption forecast. In this chapter, we aim to present some developments of artificial neural networks for energy demand side management system that determines consumers' profiles and patterns, consumption forecasting and also small generation estimations.

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“…It is noted that there are some shortcomings for BP neural network, such as falling into the local optimal value and convergence speed slow. To overcome the disadvantages, we use clone selection algorithm (CSA) [16] to optimize BP neural network. In this paper, we construct the appropriate input by the feature selection with mutual information, and then implement short-term traffic flow forecasting by the artificial neural network model, the main implementation process is shown in Fig.…”

Section: Irrelevancy Filter Stagementioning

confidence: 99%

Short-term traffic flow forecasting based on feature selection with mutual information

Yuan¹,

Tu²

2017

AIP Conference Proceedings

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Abstract. Traffic flow forecasting is related to many traffic variables, and how to select appropriate traffic variable combination is very important to traffic flow forecasting, which can reduce the cost of calculation and improve the forecasting precision. In this paper, a feature selection technique with mutual information is proposed for this purpose. Firstly, the mutual information is used to evaluate the relevance and redundancy of variables, and feature selection is used to select the relevant variables and filter out the redundancy between the selected variables. Secondly, BP neural network is used as the forecasting engine. Finally, a numerical example of traffic flow data from Pems is used to verify the forecasting performance of the proposed method, the results indicate that the proposed method can effectively reduce the cost of calculation and also improve the model forecasting precision.

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Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

Cited by 210 publications

References 42 publications

A Two-step Forecasting Solution and Upscaling Technique for Small Size Wind Farms Located in Hilly Areas of Romania

A Two-step Forecasting Solution and Upscaling Technique for Small Size Wind Farms Located in Hilly Areas of Romania

Electricity Consumption and Generation Forecasting with Artificial Neural Networks

Short-term traffic flow forecasting based on feature selection with mutual information

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