Short‐term power load forecasting based on multi‐layer bidirectional recurrent neural network

Tang, Xianlun; Dai, Yuyan; Wang, Ting; Chen, Yingjie

doi:10.1049/iet-gtd.2018.6687

Cited by 130 publications

(99 citation statements)

References 30 publications

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“…A constant nature of the power grid network system is its high variability and volatility. To mitigate this challenge and reduce the errors of the forecasting of the electric parameters, DBN is combined with Copula Model [27] and bidirectional Recurrent Neural Network [28], [29] for day and week ahead forecasting. An LSTM based hybrid DL method was tested by Motepe et al [30] for South African distribution network, showing an improved performance, considering the inclusion of temperature data.…”

Section: ) Deep Learning (Dl)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: ) Deep Learning (Dl)mentioning

confidence: 99%

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

et al. 2020

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“…Liu et al successfully applies the bidirectional LSTM (Bi-LSTM) to STLF, and concludes that the prediction results of Bi-LSTM are more accurate [12]. Tang et al proposed bidirectional GRU (Bi-GRU) to further verify that the Bi-RNN-based models are better than RNN-based models in terms of accuracy, but the efficiency is reduced, that is, the convergent time is greatly increased [13]. Currently, Recurrent neural networks (RNNs), including RNN-based models and Bi-RNN-based models, have become the most widely used models in STLF because they can effectively deal with the time series problems.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Using Recurrent Neural Networks With Input Attention Mechanism and Hidden Connection Mechanism

Zhang

2020

IEEE Access

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Short-term load forecasting is a critical task in the smart grid, which can be used to optimize power deployment and reduce power losses. Recurrent neural networks (RNNs) are the most popular deep learning models for short-term load forecasting. However, despite of achieving better forecasting accuracy than the traditional models, the performance of the existing RNN-based load forecasting approaches is still unsatisfactory for practical usage. Therefore, in this work, we have proposed input attention mechanism (IAM) and hidden connection mechanism (HCM) to greatly enhance the accuracy and efficiency of RNNbased load forecasting models. Specifically, we use IAM to assign the importance weights on input layers, which have better performances in both efficiency and accuracy than traditional attention mechanisms. To further enhance the models' efficiency, HCM is then applied to utilize residual connections to enhance the model's converging speed. We have applied both IAM and HCM on four state-of-the-art RNN implementations, and then conducted extensive experimental studies on two public datasets. Experimental results show that the proposed RNNs with IAM and HCM models achieve much better performances than the state-of-the-art baselines in both accuracy and efficiency. Ablation studies show that both IAM and HCM are essential to achieve such superior performances.

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“…Compared with time series prediction model, machine learning model is the focus of current research. Tang et al (2019) established a multi-layer bi-directional recurrent NN model to predict power load, used two groups of experimental data to verify the proposed model, and considered the difference of seasonal load. Because of the non-stationary and nonlinear characteristics of load series, it will increase the difficulty of forecasting.…”

Section: Introductionmentioning

confidence: 99%

Power load forecasting in energy system based on improved extreme learning machine

Chen

Wun

et al. 2020

Energy Exploration & Exploitation

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Through the accurate prediction of power load, the start and stop of generating units in the power grid can be arranged economically and reasonably. The safety and stability of power grid operation can be maintained. First, chicken swarm optimizer based on nonlinear dynamic convergence factor (NCSO) optimizer is proposed based on chicken swarm optimizer (CSO) optimizer. In NCSO optimizer, nonlinear dynamic inertia weight and levy mutation strategy are introduced. Compared with CSO optimizer, the convergence speed and effect of NCSO optimizer are obviously improved. Second, the random parameters of extreme learning machine (ELM) model are optimized by NCSO optimizer, and NCSOELM model is established to predict the power load. Finally, the NCSO optimization extreme learning machine (NCSOELM) model is used to predict the power load, and compared with back propagation (BP), support vector machine (SVM) and CSO optimization extreme learning machine (CSOELM) model. The experimental results show that the fitting accuracy of NCSOELM model is high, and the determination coefficient r2 is above 90%. And the root mean square error value of the NCSOELM model is 0.87, 0.41, and 0.25 smaller than the root mean square error values of the support vector machine, BP, and CSOELM models, respectively. Experiments show that the model proposed in this study has high fitting effect and low prediction error, which is of positive significance for the realization of economic and safe operation of energy system.

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Short‐term power load forecasting based on multi‐layer bidirectional recurrent neural network

Cited by 130 publications

References 30 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 Using Recurrent Neural Networks With Input Attention Mechanism and Hidden Connection Mechanism

Power load forecasting in energy system based on improved extreme learning machine

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