A Hybrid Model of EMD and PSO-SVR for Short-Term Load Forecasting in Residential Quarters

Wang, Xiping; Wang, Yaqi

doi:10.1155/2016/9895639

Cited by 28 publications

(20 citation statements)

References 36 publications

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“…For enhance the accuracy of load forecasting, EMD-PSO are garnering popularity due to the ability of EMD to decompose complicated load data into different intrinsic mode functions (IMFs), which can further be optimized by PSO. Wang et al [152] developed a load forecasting method where EMD-PSO is combined with SVR. Semero et al [153] proposed a similar approach by integrating EMD-PSO with ANFIS.…”

Section: B Empirical Mode Decomposition-particle Swarm Optimization-mentioning

confidence: 99%

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

et al. 2020

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Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand for electricity with a minimum amount of error percentage. Load prediction with less percentage of error can save millions of dollars to the utility companies. There are numerous Machine Learning (ML) techniques to amicably forecast the demand of electricity among which the hybrid models show the best result. Two or more than two predictive models are amalgamated to design a hybrid model, each of which provides improved performances by the merit of individual algorithms. This paper reviews the current stateof-the-art of electric load forecasting technologies and presents recent works pertaining to the combination of different ML algorithms into two or more methods for the construction of hybrid models. A comprehensive study of each single and multiple load forecasting model is performed with an in-depth analysis of their advantages, disadvantages, and functions. A comparison between their performance in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are developed with pertinent literature of several models to aid the researchers with the selection of suitable models for load prediction.

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Section: B Empirical Mode Decomposition-particle Swarm Optimization-mentioning

confidence: 99%

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

et al. 2020

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“…The main aim of the smart grid in power systems is energy management by the data corresponding to energy consumption/production via smart meters. Energy management in power grids reduces costs for the Optimization Algorithm [30], Empirical Mode Decomposition (EMD) with PSO-SVM [31]. In some other studies related to STLF, the hybrid approaches of Wavelet Transform (WT) with Fruit Fly Optimization (FFO) based on the Least Square Support Vector Machine (LSSVM) are utilized [32,33].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

et al. 2020

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Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model.

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“…In the past decades, experts and scholars have made systematic and effective research on traditional deterministic and probabilistic STLF and VSTLF. Deterministic forecasting methods can be divided 2 of 17 into two main categories [3]: The first category uses statistical forecasting models, such as linear regression [4], curve extrapolation [5], Autoregressive Integrated Moving Average (ARIMA) model [6,7], and other time series methods; the second category uses artificial intelligent forecasting models, such as Bayesian estimation [8], Random Forests [9], Support Vector Regression (SVR) [10,11], Artificial Neural Network (ANN) [12,13], Deep Belief Network (DBN) [14,15], and Long Short Term Memory (LSTM) Network [16,17]. These methods have achieved high forecasting accuracy and good robustness in day-ahead and hour-ahead load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting

Shi

Mei

et al. 2019

Energies

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With the refinement and intelligence of power system optimal dispatching, the widespread adoption of advanced grid applications that consider the safety and economy of power systems, and the massive access of distributed energy resources, the requirement for bus load prediction accuracy is continuously increasing. Aiming at the volatility brought about by the large-scale access of new energy sources, the adaptability to different forecasting horizons and the time series characteristics of the load, this paper proposes a phase space reconstruction (PSR) and deep belief network (DBN)-based very short-term bus load prediction model. Cross-validation is also employed to optimize the structure of the DBN. The proposed PSR-DBN very short-term bus load forecasting model is verified by applying the real measured load data of a substation. The results prove that, when compared to other alternative models, the PSR-DBN model has higher prediction accuracy and better adaptability for different forecasting horizons in the case of high distributed power penetration and large fluctuation of bus load.

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A Hybrid Model of EMD and PSO-SVR for Short-Term Load Forecasting in Residential Quarters

Cited by 28 publications

References 36 publications

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting

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