A hybrid dynamic and fuzzy time series model for mid-term power load forecasting

Lee, Woo Joo; Hong, Jinkyu

doi:10.1016/j.ijepes.2014.08.006

Cited by 121 publications

(47 citation statements)

References 23 publications

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“…The use of ANN in a hybrid manner with fuzzy and regression methods to give more flexible relations between load and load impacting variables. And, till today ANN is accepted for MTLF and LTLF [47]. Support vector regression (SVR) is the most common application form of support vector machine (SVM).…”

Section: Definition References For Mtlf and Ltlf Are Elaborated In Dmentioning

confidence: 99%

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

127

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Load forecasting has always been an important part in the planning and operation of electric utilities, i.e., both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This paper aims at reviewing the different load forecasting techniques developed for the mid-and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid-and long-term horizons, this paper reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. The study is concluded with discussion on future research directions.

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Section: Definition References For Mtlf and Ltlf Are Elaborated In Dmentioning

confidence: 99%

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

127

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“…At present, load forecasting technology has gradually transferred from traditional prediction method to artificial intelligence prediction technology. Traditional load forecasting methods, such as time series method, regression analysis method and grey prediction method [1][2][3], have some shortcomings. The forecasting accuracy of traditional methods for complex load series with larger volatility needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

2018

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Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of power system. Therefore, it is of great significance to construct a suitable model to realize the accurate prediction of the daily peak load. A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm), is proposed in this paper. Firstly, the model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm to decompose the daily peak load sequence into multiple sub sequences. Then, the model of modified grey wolf optimization and support vector machine (MGWO-SVM) is adopted to forecast the sub sequences. Finally, the forecasting sequence is reconstructed and the forecasting result is obtained. Using CEEMDAN can realize noise reduction for non-stationary daily peak load sequence, which makes the daily peak load sequence more regular. The model adopts the grey wolf optimization algorithm improved by introducing the population dynamic evolution operator and the nonlinear convergence factor to enhance the global search ability and avoid falling into the local optimum, which can better optimize the parameters of the SVM algorithm for improving the forecasting accuracy of daily peak load. In this paper, three cases are used to test the forecasting accuracy of the CEEMDAN-MGWO-SVM model. We choose the models EEMD-MGWO-SVM ( The experimental results fully demonstrate the reliability and effectiveness of the CEEMDAN-MGWO-SVM model proposed in this paper for daily peak load forecasting, which shows the strong generalization ability and robustness of the model. Keywords: daily peak load forecasting; complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); modified grey wolf optimization (MGWO); support vector machine (SVM)

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“…One is that hybrid models are gradually becoming the mainstream. Reference [16] proposes a hybrid model combining dynamic and fuzzy time series approaches to forecast the power consumption in household, commerce and industry respectively. Reference [17] utilizes an Ensemble Empirical Mode Decomposition method to extract the electricity consumption characteristics in multiple time scales, and then construct a relational model between these characteristics and the factors they affect to improve forecasting.…”

Section: Introductionmentioning

confidence: 99%

Long-term system load forecasting based on data-driven linear clustering method

Han

Yan

2017

J. Mod. Power Syst. Clean Energy

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In this paper, a data-driven linear clustering (DLC) method is proposed to solve the long-term system load forecasting problem caused by load fluctuation in some developed cities. A large substation load dataset with annual interval is utilized and firstly preprocessed by the proposed linear clustering method to prepare for modelling. Then optimal autoregressive integrated moving average (ARIMA) models are constructed for the sum series of each obtained cluster to forecast their respective future load. Finally, the system load forecasting result is obtained by summing up all the ARIMA forecasts. From error analysis and application results, it is both theoretically and practically proved that the proposed DLC method can reduce random forecasting errors while guaranteeing modelling accuracy, so that a more stable and precise system load forecasting result can be obtained.

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A hybrid dynamic and fuzzy time series model for mid-term power load forecasting

Cited by 121 publications

References 23 publications

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Long-term system load forecasting based on data-driven linear clustering method

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