A hybrid learning for neural networks applied to short term load forecasting

Topalli, Ayca Kumluca; Erkmen, I.

doi:10.1016/s0925-2312(02)00870-6

Cited by 51 publications

(14 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve the problem of forecasting electric power consumption, traditional statistical methods based on specific norms for electric power consumption and models based on expert systems and artificial neural networks can be used [6][7][8][9].…”

Section: Methodsmentioning

confidence: 99%

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

2017

View full text Add to dashboard Cite

Abstract.In article the analysis of electric power consumption and problems of power saving on coal mines are considered. Nowadays the share of conditionally constant costs of electric power for providing safe working conditions underground on coal mines is big. Therefore, the power efficiency of underground coal mining depends on electric power expense of the main technological processes and size of conditionally constant costs. The important direction of increase of power efficiency of coal mining is forecasting of a power consumption and monitoring of electric power expense. One of the main approaches to reducing of electric power costs is increase in accuracy of the enterprise demand in the wholesale electric power market. It is offered to use artificial neural networks to forecasting of day-ahead power consumption with hourly breakdown. At the same time use of neural and indistinct (hybrid) systems on the principles of fuzzy logic, neural networks and genetic algorithms is more preferable. This model allows to do exact short-term forecasts at a small array of input data. A set of the input parameters characterizing mining-and-geological and technological features of the enterprise is offered.

show abstract

Section: Methodsmentioning

confidence: 99%

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

2017

View full text Add to dashboard Cite

show abstract

“…The short-term load forecasting means that the unit is the month within one year, also refers to weeks, days, hours of load forecasting, it is mainly used for power system dispatching. The accurate short-term load forecasting results are helpful to make a proper plan of electric power volume, propose the appropriate running project and bidding strategy, and also promote the electricity plan management, section coal, fuel efficiency and reduce power generation cost, make the reasonable construction plan, improve the economic benefit and social benefit of electric power system [3][4][5][6][7]. Because the selection of some key parameters in the SVM directly affect the predicted results and the widespread of the forecasting model ,so it has not yet formed a unified model.…”

Section: Introductionmentioning

confidence: 99%

Study on Short-Term Load Forecasting Method Based on the PSO and SVM Model

Jiang¹

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…For example, Bianco et al [2] proposed linear regression models for electricity consumption forecasting; Zhou et al [3] applied a grey prediction model for energy consumption; Afshar and Bigdeli [4] proposed an improved singular spectral analysis method for short-term load forecasting (STLF) for the Iranian electricity market; and Kumar and Jain [5] applied three time series models-Grey-Markov model, Grey-Model with rolling mechanism, and singular spectrum analysis-to forecast the consumption of conventional energy in India. By employing artificial neural networks, references [6][7][8][9] proposed several useful short-term load forecasting models. By hybridizing the popular method and evolutionary algorithm, the authors of [10][11][12][13] demonstrated further performance improvements which could be made for energy forecasting.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

et al. 2013

View full text Add to dashboard Cite

Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) for electric load forecasting. The electric load data of the New South Wales (Australia) market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

show abstract

A hybrid learning for neural networks applied to short term load forecasting

Cited by 51 publications

References 1 publication

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

Study on Short-Term Load Forecasting Method Based on the PSO and SVM Model

Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

Contact Info

Product

Resources

About