Short-Term Electrical Load Forecasting With Multidimensional Feature Extraction

Kim, Nakyoung; Park, Hyunseo; Lee, Joohyung; Choi, Jun Kyun

doi:10.1109/tsg.2022.3158387

Cited by 29 publications

(6 citation statements)

References 39 publications

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“…In this hybrid forecasting system, the optimization search process of the MVO algorithm used the Root Mean Square Error (RMSE) and the similarity (R) of the prediction curves to obtain the fitness value (NI) to determine the number of nodes in the implicit layer in the prediction model. In addition, the Root Mean Square (MAE), Mean Absolute Percentage Error (MAPE) and Nash-Sutcliffe efficiency coefficient (NSE) were selected, which together with RMSE and R to evaluate the predictive performance of the proposed model [44][45][46]. The parametric optimization of the MVO algorithmic process is shown in Figure 5.…”

Section: Hybrid Forecasting Systemmentioning

confidence: 99%

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

et al. 2022

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Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Multiverse optimization algorithm (MVO), and the Gated Recurrent Unit (GRU) based on Rectified Adam (RAdam) optimizer. Firstly, the model uses the CEEMDAN algorithm to decompose the original electric load data into subsequences of different frequencies, and the dominant factors are extracted from the subsequences. Then, a GRU network based on the RAdam optimizer was built to perform the forecasting of the subsequences using the existing subsequences data and the associated influencing factors as the data set. Meanwhile, the parameters of the GRU network were optimized with the MVO optimization algorithm for the prediction problems of different subsequences. Finally, the prediction results of each subsequence were superimposed to obtain the final prediction results. The proposed combined prediction method was implemented in a case study of a substation in Weinan, China, and the prediction accuracy was compared with the traditional prediction method. The prediction accuracy index shows that the Root Mean Square Error of the prediction results of the proposed model is 80.18% lower than that of the traditional method, and the prediction accuracy error is controlled within 2%, indicating that the proposed model is better than the traditional method. This will have a favorable impact on the safe and stable operation of the power grid.

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Section: Hybrid Forecasting Systemmentioning

confidence: 99%

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

et al. 2022

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“…Load forecasting also helps optimize resource allocation, reduce operating costs, and improve overall system efficiency. It can be said that load forecasting is an indispensable part of power system planning and scheduling, as it relates to the stability and sustainability of power supply, and has significant impacts on economic and social development [2]. Therefore, load forecasting has always been a critical issue in the field of electricity.…”

Section: Introductionmentioning

confidence: 99%

A Combined Method for Short-Term Load Forecasting Considering the Characteristics of Components of Seasonal and Trend Decomposition Using Local Regression

Hu,

Wang,

Cai

et al. 2024

Applied Sciences

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In response to the complexity and high volatility of original load data affecting the accuracy of load forecasting, a combined method for short-term load forecasting considering the characteristics of components of seasonal and trend decomposition using local regression (STL) is proposed. The original load data are decomposed into a trend component, seasonal component, and residual component using STL. Then, considering the characteristics of each component, a long short-term memory (LSTM) neural network, a convolutional neural network (CNN), and Gaussian process regression (GPR) are used to predict the trend component, seasonal component, and residual component, respectively. The final outcome of the load forecasting is obtained by summing the forecasted results of each individual component. A specific case study is conducted to compare the proposed combined method with LSTM, CNN, GPR, STL-LSTM, STL-CNN, and STL-GPR prediction methods. Through comparison, the proposed combined method exhibits lower errors and higher accuracy, demonstrating the effectiveness of this method.

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“…On the one hand, the leap forward development of renewable energy significantly increases the variability of system operation mode, aggravating the complexity of forecasting environment [5,6]. On the other hand, short-term load presents stronger nonlinearity and randomness due to the influence of climate, economy, and other factors, which makes the power consumption patterns more difficult to capture [7]. Therefore, it has become a growing concern for achieving accurate short-term load forecasting of distribution networks.…”

Section: Introductionmentioning

confidence: 99%

A hybrid deep learning model for short‐term load forecasting of distribution networks integrating the channel attention mechanism

Qin,

Gao,

Ding

et al. 2024

IET Generation Trans & Dist

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Optimizing short‐term load forecasting performance is a challenge due to the randomness of nonlinear power load and variability of system operation mode. The existing methods generally ignore how to reasonably and effectively combine the complementary advantages among them and fail to capture enough internal information from load data, resulting in accuracy reduction. To achieve accurate and efficient short‐term load forecasting, an integral implementation framework is proposed based on convolutional neural network (CNN), gated recurrent unit (GRU) and channel attention mechanism. CNN and GRU are first combined to fully extract the highly complicated dynamic characteristics and learn time compliance relationships of load sequence. Based on CNN‐GRU network, the channel attention mechanism is introduced to further reduce the loss of historical information and enhance the impact of important characteristics. Then, the overall framework of short‐term load forecasting based on CNN‐GRU‐Attention network is proposed, and the coupling relationship between each stage is revealed. Finally, the developed framework is implemented on realistic load dataset of distribution networks, and the experimental results verify the effectiveness of the proposed method. Compared with the state‐of‐the‐art models, the CNN‐GRU‐Attention model outperforms in different evaluation metrics.

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Short-Term Electrical Load Forecasting With Multidimensional Feature Extraction

Cited by 29 publications

References 39 publications

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

A Combined Method for Short-Term Load Forecasting Considering the Characteristics of Components of Seasonal and Trend Decomposition Using Local Regression

A hybrid deep learning model for short‐term load forecasting of distribution networks integrating the channel attention mechanism

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