High-Precision Power Load Forecasting Using Real-time Temperature Information and Deep Learning Method

Xu, Xianfeng; Gong, Mei; Huang, Liuyang; Liu, Ahui

doi:10.1080/15325008.2019.1689451

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…The training seeks to achieve the minimization of the multi-step loss function in Eqs. (10) and (11). The training of this model uses Adam optimizer to iteratively update the coefficients of each layer in the model to obtain the final dualattention mechanism LSTM load forecasting model.…”

Section: Model Trainingmentioning

confidence: 99%

“…The input here is the weighted feature value of the influences filtered by RFE and then considering the magnitude of the correlation.𝐿𝑆𝑇𝑀𝑐𝑒𝑙𝑙 It represents a network unit of LSTM [10][11][12]. The input layer considers the correlation between the input-related feature and the output load through the feature attention mechanism, and the key factors affecting the load output are weighted.…”

mentioning

confidence: 99%

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Dual attention LSTM for building power load forecasting based on feature selection

Bin¹,

Yang

Wang

2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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Load forecasting plays an important role in ensuring the safe operation of the power grid. Accurate load forecasting is mainly influenced by historical load data, PV, precipitation and other factors. A load forecasting model with double attention LSTM based on feature selection is proposed, which is designed to comprehensively solve the impact of multiple factors on load forecasting. This model uses Recursive Feature Elimination to remove redundant influencing factors and outputs features with high correlation to the real load. Based on the Long-Short Term Memory network, the feature selection and timing dual attention mechanisms are introduced to merge and dynamically explore the connection between load and input features, which then improves the accuracy of load forecasting. The experimental results show that the accuracy of the proposed model for load forecasting is significantly improved compared with the traditional model.

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Section: Model Trainingmentioning

confidence: 99%

mentioning

confidence: 99%

Dual attention LSTM for building power load forecasting based on feature selection

Bin¹,

Yang

Wang

2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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show abstract

“…Backpropagation (BP) and long short-term memory (LSTM) neural network models are representative and widely used. For example, some studies (Xu et al, 2017;Xu et al, 2019;Zheng et al, 2020) predicted the electricity consumption index based on the LSTM model, while other studies (Beccali et al, 2011;Shobha and Balasaranya, 2012) used the artificial neural network model and the RBFN neural network to predict residential electricity consumption and household air conditioning power consumption in the Mediterranean region. Subsequently, the combination prediction of the neural network combined with traditional methods also occurred, such as the combination of the gray theory and artificial neural network (Chen, 2019) for electricity consumption prediction tests.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the whole society electricity consumption in northeast China based on the BP neural network and Markov

Yan,

Shao,

Wang

et al. 2024

Front. Energy Res.

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Northeast China has been facing a severe power shortage situation. Since September 2021, “power rationing” events occurring in many places in the three provinces of northeast China have been causing inconvenience to people’s production and life. Therefore, it is particularly important to accurately predict the power load combined with the influencing factors of local power consumption. At the same time, the northeast region is about to enter the heating season, and the pressure on coal and electricity will further increase. In Heilongjiang Province, due to coal capacity control, limited production led to the high price of thermal coal; wind power photovoltaic output fluctuations, the epidemic, and other reasons also resulted in a large gap in the power supply side. Improving the power demand forecasting ability is of great significance to strengthen the reliability of people’s daily electricity consumption, rational distribution of power generation plans, and deployment of power grid resources. In order to improve the accuracy of electricity consumption prediction in Heilongjiang Province, Markov error correction is carried out on the basis of the backpropagation (BP) neural network prediction model so that the final prediction results have the advantages of the BP neural network prediction model and Markov model. In addition, it is more suitable for the prediction of random series data with high volatility, the prediction accuracy can be improved significantly, and the overall trend of electricity consumption can be predicted more accurately.

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“…Because of the nonlinear relationship between load demand and weather conditions, traditional methods such as the autoregressive moving average model (Saab et al , 2001; Amarawickrama and Hunt, 2008), exponential smoothing models (Douglas et al , 1998) and regression-based models (Chen et al , 2017; Reddy and Vishali, 2017) have some shortcomings in load prediction. Then, the researchers applied machine learning (ML), deep learning (Xu et al , 2019; Azad et al , 2018) and other artificial intelligence-based methods (Hu et al , 2013), which were able to achieve high accuracy in load forecasting.…”

Section: Introductionmentioning

confidence: 99%

A machine learning-driven support vector regression model for enhanced generation system reliability prediction

Bolourchi,

Gholami

2023

COMPEL

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Purpose The purpose of this paper is to achieve high accuracy in forecasting generation reliability by accurately evaluating the reliability of power systems. This study uses the RTS-79 reliability test system to measure the method’s effectiveness, using mean absolute percentage error as the performance metrics. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance, making this study relevant to power system planning and management. Design/methodology/approach This paper proposes a novel approach that uses a radial basis kernel function-based support vector regression method to accurately evaluate the reliability of power systems. The approach selects relevant system features and computes loss of load expectation (LOLE) and expected energy not supplied (EENS) using the analytical unit additional algorithm. The proposed method is evaluated under two scenarios, with changes applied to the load demand side or both the generation system and load profile. Findings The proposed method predicts LOLE and EENS with high accuracy, especially in the first scenario. The results demonstrate the method’s effectiveness in forecasting generation reliability. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance. Therefore, the findings of this study have significant implications for power system planning and management. Originality/value What sets this approach apart is the extraction of several features from both the generation and load sides of the power system, representing a unique contribution to the field.

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High-Precision Power Load Forecasting Using Real-time Temperature Information and Deep Learning Method

Cited by 11 publications

References 10 publications

Dual attention LSTM for building power load forecasting based on feature selection

Dual attention LSTM for building power load forecasting based on feature selection

Prediction of the whole society electricity consumption in northeast China based on the BP neural network and Markov

A machine learning-driven support vector regression model for enhanced generation system reliability prediction

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