Short-Term Load Forecasting Using a Novel Deep Learning Framework

Zhang, Xiaoyu; Wang, Rui; Zhang, Tao; Liu, Huan; Zha, Yabing

doi:10.3390/en11061554

Cited by 20 publications

(13 citation statements)

References 36 publications

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“…where weight matrix w ij is associated with the connection between the hidden unit h j and visible unit v i . Overall, there are many attractive implementations and uses of RBM, 36,70 DBN, [70][71][72][73][74] and DBM 75 in load data analytics.…”

Section: Techniques Applied To These Challengesmentioning

confidence: 99%

Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges

Elahe

Jin

Zeng

2021

Intl J of Energy Research

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The collection and storage of large-scale load data in a smart grid provide new approaches for the efficient, economical, and safe operation of power systems.Deep Learning (DL) has become increasingly popular for large-scale load data analytics in recent years because of its ability to extract latent features and discovering complex relationships. This paper first overviews eight typical open load datasets of the grid and smart meter collected worldwide, the challenges faced by conventional machine learning, and the DL techniques applied to these challenges. A comprehensive review of the applications of DL techniques is then conducted from the perspective of analysis, forecast, management, and presented observation on each application. Critical points of DL models for improving performance are further discussed. In conclusion, several pressing problems of DL in load data analytics are identified, such as the accuracy gap between the actual and the expected, the generalization of hyperparameter setting, and the interpretation mechanism of DL output, which need special attention.

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Section: Techniques Applied To These Challengesmentioning

confidence: 99%

Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges

Elahe

Jin

Zeng

2021

Intl J of Energy Research

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“…RBM, a stochastic binary structure, can learn the distribution characteristics of sample data [34,35]. This binary structure consists of visible layers and hidden layers.…”

Section: Pre-training Processmentioning

confidence: 99%

Short-Term Load Interval Prediction Using a Deep Belief Network

et al. 2018

Self Cite

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In load predication, point-based forecasting methods have been widely applied. However, uncertainties arising in load predication bring significant challenges for such methods. This therefore drives the development of new methods amongst which interval predication is one of the most effective. In this study, a deep belief network-based lower–upper bound estimation (LUBE) approach is proposed, and a genetic algorithm is applied to reinforce the search ability of the LUBE method, instead of simulated an annealing algorithm. The approach is applied to the short-term load prediction on some realistic electricity load data. To demonstrate the effectiveness and efficiency of the proposed method, it is compared with three state-of-the-art methods. Experimental results show that the proposed approach can significantly improve the predication accuracy.

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“…And a kind of scalable deep learning is proposed for real-time economic power generation scheduling and control based on the three-state energy of the future smart grid. Zhang et al [25] presented a prediction model based on the restricted Boltzmann machine and Elman. He et al [26] proposed a deep belief network (DBN) embedded in a parametric copula model.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization

Wang

Bai

et al. 2020

Complexity

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The power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load data are decomposed into components; then, the gated recurrent unit (GRU) network, with the Bayesian optimization parameters, is used as the subpredictor for each component. Last, the predictions of different components are fused to achieve the final predictions. The power load data of American Electric Power (AEP) were used to verify the proposed predictor. The results showed that the proposed prediction method could effectively improve the accuracy of power load prediction.

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Short-Term Load Forecasting Using a Novel Deep Learning Framework

Cited by 20 publications

References 36 publications

Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges

Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges

Short-Term Load Interval Prediction Using a Deep Belief Network

Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization

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