Short-Term Load Forecasting for the Holidays Using Fuzzy Linear Regression Method

Song, Kyung‐Bin; Baek, Young-Sik; Hong, Dug Hun; Jang, Gilsoo

doi:10.1109/tpwrs.2004.835632

Cited by 415 publications

(163 citation statements)

References 14 publications

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“…However, these techniques have a possibility to lack the accuracy of prediction with the higher load forecasting errors in some particular time zones, which are, for example, the weekdays of the summer season, weekend, and/or Monday. To overcome this problem, the computational intelligence techniques [16]- [24], which are the fuzzy systems and artificial neural networks [19,22,27], have been investigated in the past decade as an alternative to the conventional methods.…”

Section: Hybrid Algorithmmentioning

confidence: 99%

Artificial Neural Network Based Hybrid Algorithmic Structure for Solving Linear Programming Problems

Babu¹,

Palaniappan²

2010

IJCEE

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Section: Hybrid Algorithmmentioning

confidence: 99%

Artificial Neural Network Based Hybrid Algorithmic Structure for Solving Linear Programming Problems

Babu¹,

Palaniappan²

2010

IJCEE

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“…[18][19]. In [20] a new method based on fuzzy regression analysis is introduced for short-term load forecasting error for 24 hours during the holidays. Authors believed that the average load forecasting error can be higher compared to the other days during week.…”

Section: Introductionmentioning

confidence: 99%

A Novel Combined Approach for Daily Electric Load Forecasting Based on Artificial Neural Network and Modified Bat Algorithm

Capra¹,

Ribeiro²

2017

IJCATR

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Abstract:In this paper a novel combined method based on Modified Bat Algorithm (MBA) and Neural Network algorithm has proposed in order to forecast the electric peak load power. In the proposed method, Bat Algorithm is employed as a popular optimization method and Artificial Neural Network is also utilized as a powerful mathematic method in mapping nonlinear relationship among model variables for the purpose of electric daily load prediction. Additionally, in order to improve the performance of the bat algorithm with regards to avoiding tapping into the local optimal and increasing the convergence speed, some modification has performed in bat algorithm which is called as SAMBA. Experimental results indicate that the proposed method has superiority performance in comparison with other traditional machine learning algorithms

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“…Several methods have been already proposed for the forecasting of the wind speed using different techniques such as linear regression analysis, time series, fuzzy linear regression method etc. [3][4][5].In addition, smoothing of output power of wind farm (WF) can be also achieved considering different kinds of energy storage devices. In general, Battery energy storage system (BESS), super conducting magnetic energy storage systems (SMES), energy capacitor systems (ECS) or flywheel energy storage systems (FESS) etc.…”

Section: Introductionmentioning

confidence: 99%

A new control strategy for smoothing of wind farm output using short-term ahead wind speed prediction and Flywheel energy storage system

Islam

Hasanien

Al‐Durra

et al. 2012

2012 American Control Conference (ACC)

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Abstract-With the increased integration of wind energy into power networks it has become more important to have a reliable system for mitigating the fluctuations of output power supplied to the grid. This paper proposes a new control scheme to smooth the output power fluctuations of an aggregated wind firm using Flywheel energy storage system (FESS) and shortterm ahead prediction of wind speed. In the proposed system, the kinetic energy of the FEES is utilized to smooth the output power fluctuations of wind farm. In addition, the stored energy of FESS is utilized in most efficient way by correcting the power reference using prediction base control. This helps to reduce the overall system cost by keeping the size of the energy storage system at minimum. The effectiveness of the proposed control is verified by using PSCAD/EMTDC.

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Short-Term Load Forecasting for the Holidays Using Fuzzy Linear Regression Method

Cited by 415 publications

References 14 publications

Artificial Neural Network Based Hybrid Algorithmic Structure for Solving Linear Programming Problems

Artificial Neural Network Based Hybrid Algorithmic Structure for Solving Linear Programming Problems

A Novel Combined Approach for Daily Electric Load Forecasting Based on Artificial Neural Network and Modified Bat Algorithm

A new control strategy for smoothing of wind farm output using short-term ahead wind speed prediction and Flywheel energy storage system

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