Empirical Mode Decomposition based Multi-objective Deep Belief Network for short-term power load forecasting

Influenced by many uncertain and random factors, nonstationary, nonlinearity, and time-variety appear in power load series, which is difficult to forecast accurately. Aiming at locating these issues of power load forecasting, an innovative hybrid method is proposed to forecast power load in this paper. Firstly, ensemble empirical mode decomposition (EEMD) is used to decompose the power load series into a series of independent intrinsic mode functions (IMFs) and a residual term. Secondly, genetic algorithm (GA) is then applied to determine the best weights of each IMF and the residual term named ensemble empirical mode decomposition based on weight (WEEMD). Thirdly, least square support vector machine (LSSVM) and nonparametric generalized autoregressive conditional heteroscedasticity (NPGARCH) are employed to forecast the subseries, respectively, based on the characteristics of power load series. Finally, the forecasted power load of each component is summed as the final forecasted result of power load. Compared with other methods, the forecasting results of this proposed model applied to the electricity market of Pennsylvania-New Jersey-Maryland (PJM) indicate that the proposed model outperforms other models.

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Application of Variational Mode Decomposition and Deep Learning in Short-Term Power Load Forecasting

Journal of Physics Conference Series ◽

10.1088/1742-6596/1883/1/012128 ◽

2021 ◽

Vol 1883 (1) ◽

pp. 012128

Author(s):

Ping Yu ◽

Jie Fang ◽

Yubao Xu ◽

Qiang Shi

Keyword(s):

Deep Learning ◽

Load Forecasting ◽

Variational Mode Decomposition ◽

Short Term ◽

Mode Decomposition ◽

Power Load ◽

Power Load Forecasting

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Short-Term Power Load Forecasting Based on EMD and ESN

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.651.910 ◽

2013 ◽

Vol 651 ◽

pp. 910-916

Author(s):

Yong Luo ◽

Xue Jia ◽

Shu Wei Chen

Keyword(s):

Prediction Accuracy ◽

Load Forecasting ◽

Original Data ◽

Echo State Network ◽

Short Term ◽

Independent Components ◽

Mode Decomposition ◽

Power Load ◽

Power Load Forecasting ◽

Network Method

With the continuous development of power market, the precision requirement for short-term power load forecasting is constantly being improved. In order to obtain higher prediction accuracy, this paper put forward a method of combining empirical mode decomposition (EMD) with echo state network (ESN) for short-term power load forecasting. First, original data had been decomposed into several independent components, whose features were obvious. A corresponding echo state network was built for each component. Then, each component should be trained and predicted by its corresponding echo state network. The experimental results showed that this method has a better prediction accuracy compared with traditional neural network method.

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Forecasting for Ultra-Short-Term Electric Power Load Based on Integrated Artificial Neural Networks

Symmetry ◽

10.3390/sym11081063 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1063 ◽

Cited By ~ 2

Author(s):

Horng-Lin Shieh ◽

Fu-Hsien Chen

Keyword(s):

Energy Efficiency ◽

Electric Power ◽

Production Efficiency ◽

Load Forecasting ◽

Short Term ◽

Inference System ◽

Power Load ◽

Power Load Forecasting ◽

New Energy ◽

Electric Power Load

Energy efficiency and renewable energy are the two main research topics for sustainable energy. In the past ten years, countries around the world have invested a lot of manpower into new energy research. However, in addition to new energy development, energy efficiency technologies need to be emphasized to promote production efficiency and reduce environmental pollution. In order to improve power production efficiency, an integrated solution regarding the issue of electric power load forecasting was proposed in this study. The solution proposed was to, in combination with persistence and search algorithms, establish a new integrated ultra-short-term electric power load forecasting method based on the adaptive-network-based fuzzy inference system (ANFIS) and back-propagation neural network (BPN), which can be applied in forecasting electric power load in Taiwan. The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying ANFIS, BPN and persistence. The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and finally the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting. In this paper, the optimal electric power load value was forecasted based on the weighted value obtained therefrom. It was proven through experimental results that the solution proposed in this paper can be used to accurately forecast electric power load, with a minimal error.

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