Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function

You, He; Xu, Qifa; Wan, Junwei; Yang, Shanlin

doi:10.1016/j.energy.2016.08.023

Cited by 92 publications

(28 citation statements)

References 34 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The literature has proposed many novel methods for short-term load forecasting like fuzzy [18], exponential smoothing [19], regression based [20], neural networks [21], and others. Moreover, every proposed model has incorporated some techniques.…”

Section: Related Workmentioning

confidence: 99%

“…The maximum profit of any U C is fluctuating in relation to energy for a constant κ uc 0 . According to Equation (20), this phenomenon leads to parameters of equality. Every U C proffers to vend all its energy to consumers.…”

Section: Analysis Of Utility Companiesmentioning

confidence: 99%

“…Furthermore, E prod,uc 0 is constant in κ uc 0 as discussed in Equation (20). Subsequently, the f (E prod,uc 0 ) according to κ uc 0 is,…”

Section: Proof Of Theoremmentioning

confidence: 99%

See 2 more Smart Citations

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

et al. 2018

View full text Add to dashboard Cite

Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Analysis Of Utility Companiesmentioning

confidence: 99%

See 1 more Smart Citation

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Accurate STLF predictions play a vital role in electrical department load dispatch, unit commitment, and electricity market trading [1]. With the permeation of renewable resources in grids and the technological innovation of electric vehicles, load components become more complex and make STLF difficult; therefore, strict requirements of stability and accuracy are needed [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours

Lin

et al. 2018

Energies

View full text Add to dashboard Cite

Abstract:To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to the previous 168 h considering the calendar feature. Second, a feature selection combined with a predictor process was carried out to select the optimal feature for building a reliable predictor with respect to each hour. The final modular model consisted of 24 predictors with a respective optimal feature subset for day-ahead load forecasting. New England and Singapore load data were used to evaluate the effectiveness of the proposed method. The results indicated that the accuracy of the proposed modular model was higher than that of the traditional method. Furthermore, conducting a feature selection step when building a predictor improved the accuracy of load forecasting.

show abstract

“…It is a prerequisite for the economic operation of power systems and the basis of dispatching and making startup-shutdown plans, which plays a key role in the automatic control of power systems [1][2][3]. Accurate power load forecasting not only helps users choose a more appropriate electricity consumption scheme and reduces a lot of electric cost expenditure while improving equipment utilization thus reducing the production cost and improving the economic benefit, but also is conducive to optimizing the resources of power systems, improving power supply capability and ultimately achieving the aim of energy conservation and emission reduction [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

Fan

Duan

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval.

show abstract

Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function

Cited by 92 publications

References 34 publications

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

Contact Info

Product

Resources

About