WRL: A Combined Model for Short-Term Load Forecasting

Liu, Yuecan; Zhang, Kun; Zhen, Shuai; Yongming, Guan; Shi, Yuliang

doi:10.1007/978-3-030-26072-9_3

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Liu et al utilized the combination of wavelet decomposition, radial basis function, and bidirectional LSTM to predict electric energy consumption. The experimental results indicate that bidirectional LSTM framework outperforms the unidirectional approaches in terms of several performance metrics for electric prediction [12]. Deng et al proposed a deep learning framework based on bidirectional gated recurrent unit for wind power prediction to improve the accuracy by making full use of the information provided by multiple data sources of numerical weather forecast.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Bidirectional RNN-based models (Bi-RNN-based models) are proposed to achieve higher accuracy by taking the historical and future information into consideration [11]. 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].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

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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“…Finally, the forecasting result and the adjustment values are used as the input to the LSTM network (Gers, Schmidhuber, and Cummins 1999) to perform regression forecasting and wavelet inverse transformation to obtain the best forecasting results. More details of WD-LSTM can be founded in (Liu et al 2019;Zheng et al 2019).…”

Section: Short-term Load Forecastingmentioning

confidence: 99%

“…5. WD-LSTM (Liu et al 2019;Zheng et al 2019): this is our proposed approach for short-term power consumption gap prediction designed to address the specific challenges facing Chinese power consumption management systems, which was described in the previous section.…”

Section: Application Development and Deploymentmentioning

confidence: 99%

PIDS: An Intelligent Electric Power Management Platform

Zheng

Shi

et al. 2020

AAAI

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Electricity information tracking systems are increasingly being adopted across China. Such systems can collect real-time power consumption data from users, and provide opportunities for artificial intelligence (AI) to help power companies and authorities make optimal demand-side management decisions. In this paper, we discuss power utilization improvement in Shandong Province, China with a deployed AI application - the Power Intelligent Decision Support (PIDS) platform. Based on improved short-term power consumption gap prediction, PIDS uses an optimal power adjustment plan which enables fine-grained Demand Response (DR) and Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. Deployed in August 2018, the platform is helping over 400 companies optimize their power consumption through DR while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS through planned shutdown has been reduced from 16% to 0.56%, resulting in significant gains in economic activities.

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