Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model

Al-Hamadi, H.M.; Soliman, S. A.

doi:10.1016/s0378-7796(03)00150-0

Cited by 249 publications

(122 citation statements)

References 13 publications

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“…In the last few decades, models for improving the accuracy of load forecasting have included the well-known Box-Jenkins' ARIMA model [6], exponential smoothing model [7], Kalman filtering/ linear quadratic estimation model [8][9][10], the Bayesian estimation model [11][12][13], and regression models [14][15][16]. However, most of these models are theoretically based on assumed linear relationships between historical data and exogenous variables and so cannot effectively capture the complex nonlinear characteristics of load series, or easily provide highly accurate load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Lee

Lin

2016

Energies

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Abstract:Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory) to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS) to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.

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Section: Introductionmentioning

confidence: 99%

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Lee

Lin

2016

Energies

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“…These previous studies were based mainly on traditional statistical analyses such as multiple regression techniques; however, air temperature or related variables such as heating degree-days have been incorporated into the regression models (e.g., Papalexopoulos and Hesterberg, 1990;Park et al, 1991;Soliman et al, 1997;Ružić et al, 2003;Al-Hamadi and Soliman, 2004;Mirasgedis et al, 2006;Niu et al, 2010). In such linear regression studies, only one or two weather parameters other than temperature were considered, and therefore, it is difficult to assess the independent relationships between electric power demand and weather conditions properly.…”

Section: Introductionmentioning

confidence: 99%

Weather impacts on electric power load: partial phase synchronization analysis

Hong

Kim

2015

Meteorological Applications

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Electricity demand is influenced by atmospheric conditions, and, therefore it is important to quantify their relationships suitably for accurate electricity demand forecasting and the implementation of power-saving policies. However, interdependencies and characteristics of covariance among meteorological variables within the same periodicities hinder the quantification of their direct and indirect impacts on electric power load. To investigate the strength of the direct correlation between atmospheric conditions and electric power load, this study harnessed a new partialization analysis method based on a partial phase synchronization index combined with wavelet transformation. The advantage of the proposed method is that it can be used to evaluate the degree of independent contribution of the variables over different spatiotemporal scales. Compared with traditional statistical analyses, this new partialization analysis shows that air temperature is the principal variable associated directly with electricity demand, but that the strength of the relationship varies with season and time scale. Relative humidity and wind speed have strong direct correlations with electricity in summer and winter, respectively. Insolation is directly coupled to the electric power load only on sub-diurnal time scales. This investigation indicates that for accurate forecasting of electricity demand, changes in the coupling strengths of different atmospheric variables should be incorporated into the electric power load forecasting process. The study shows that a partial phase synchronization index, combined with wavelet transformation, is a useful tool that could be used in other studies to assess complex interacting atmospheric oscillations that cannot be assessed properly by traditional approaches.

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“…Load forecasting has always been an essential and important topic for power systems, especially the STLF [1]. Basic operation functions such as unit commitment, economic dispatch, fuel scheduling, and unit maintenance can be performed more efficiently with an accurate forecasting [2].…”

Section: Introductionmentioning

confidence: 99%

Short-term Load forecasting by a new hybrid model

Guo¹,

Du²,

Wu³

et al. 2013

Advances in Intelligent Systems Research

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Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model

Cited by 249 publications

References 13 publications

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Weather impacts on electric power load: partial phase synchronization analysis

Short-term Load forecasting by a new hybrid model

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