Fuzzy short-term electric load forecasting using Kalman filter

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

doi:10.1049/ip-gtd:20050088

Cited by 56 publications

(19 citation statements)

References 19 publications

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“…A simple prediction of fuzzy model in [22,23] proves that it could provide a very satisfying prediction error. Other fuzzy logic techniques and combined approach are discussed in [24][25][26][27]. The method of particle swarm [28], support vector machines [29] and neurofuzzy [30,31] had also shown relatively good forecast accuracy.…”

Section: Introductionmentioning

confidence: 99%

Autoregressive method in short term load forecast

Baharudin

Kamel

2008

2008 IEEE 2nd International Power and Energy Conference

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Short-term load forecasting plays an important role in planning and operation of power system. The accuracy of this forecasted value is necessary for economically efficient operation and also for effective control. This paper describes the methods of autoregressive (AR) Burg's and modified covariance (MCOV) in solving a short term load forecast. The methods are tested based on historical load data of New South Wales, Australia. The accuracy of discussed methods are obtained and reported.

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Section: Introductionmentioning

confidence: 99%

Autoregressive method in short term load forecast

Baharudin

Kamel

2008

2008 IEEE 2nd International Power and Energy Conference

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“…General forecasting methods can be divided into two branches: the statistical method and the artificial intelligence method. Statistical methods such as regression analysis, exponential smoothing, Kalman filter, and autoregressive integrated moving average (ARIMA) are easy to apply but modeling is difficult for complex loads [7][8][9]. Artificial intelligence methods show better performance than statistical methods in load forecasting and include fuzzy logic, the artificial neural network (ANN), the support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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

Lin

et al. 2018

Energies

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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.

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“…In the traditional methods, such as autoregressive integrated moving average (ARIMA) [3] and regression analysis [4], Kalman filter [5] and exponential smoothing [6] are commonly used. The combination of autoregressive and moving average in ARIMA is a better time series model for STLF [7].…”

Section: Introductionmentioning

confidence: 99%

Short Term Electrical Load Forecasting Using Mutual Information Based Feature Selection with Generalized Minimum-Redundancy and Maximum-Relevance Criteria

Huang

Cai

et al. 2016

Entropy

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A feature selection method based on the generalized minimum redundancy and maximum relevance (G-mRMR) is proposed to improve the accuracy of short-term load forecasting (STLF). First, mutual information is calculated to analyze the relations between the original features and the load sequence, as well as the redundancy among the original features. Second, a weighting factor selected by statistical experiments is used to balance the relevance and redundancy of features when using the G-mRMR. Third, each feature is ranked in a descending order according to its relevance and redundancy as computed by G-mRMR. A sequential forward selection method is utilized for choosing the optimal subset. Finally, a STLF predictor is constructed based on random forest with the obtained optimal subset. The effectiveness and improvement of the proposed method was tested with actual load data.

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Fuzzy short-term electric load forecasting using Kalman filter

Cited by 56 publications

References 19 publications

Autoregressive method in short term load forecast

Autoregressive method in short term load forecast

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

Short Term Electrical Load Forecasting Using Mutual Information Based Feature Selection with Generalized Minimum-Redundancy and Maximum-Relevance Criteria

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