Research on short-term power load forecasting based on Elman neural network with Genetic Algorithm

Sun, Yijie; Xia, Huiwen

doi:10.1109/cac57257.2022.10055581

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…And the errors are all less than 9%. Compared with the models based on SVM 14 and LSTM 11 , the proposed heat load forecasting model based on DBN has higher accuracy. For heat exchange station A, its forecasting error is 4.83% lower than that of SVM-based forecasting model and 2.05% lower than that of LSTM-based forecasting model.…”

Section: Experimental Verification and Results Analysismentioning

confidence: 97%

“…For example, Zhang et al establish a Long Short-Term Memory Neural Network (LSTM) model with outdoor temperature and wind power as the main influencing factors by analysis, and predicted the primary side heat load of a district heating system within 24 hours 10 . Sun et al propose a heat load prediction model based on double layer long short-term memory (LSTM) network, and try to improve the prediction accuracy by modifying the loss function 11 . Wu et al propose a prediction method based on MMoE multi-task learning and long short-term memory network (LSTM), which predicted the comprehensive energy system load including short term heat load7.…”

Section: Introductionmentioning

confidence: 99%

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Short term heat load forecasting method based on deep belief network

Wang,

Zhao,

Ding

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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In order to avoid waste of heat energy, improve energy efficiency and reduce heating costs, this paper proposes a shortterm heat load forecasting method based on a deep belief network (DBN). It utilizes the historical heat supply heat data, historical meteorological data, and weather forecast data to predict the user-side heating load in the next 24 hours. First, the correlation coefficient method is used to determine the meteorological data which has a greater impact on the shortterm heat load forecast. Second, the 3σ method is used to find and replace the outliers data, and the standardization is processed to form the experimental dataset. Then, using the divided data set, the heat load forecasting model is trained offline and verified, and the parameters are optimized. Finally, the historical heat load data and meteorological data within 72 hours and the weather forecast data within the next 24 hours are used as input to predict the heating load within the next 24 hours online. The experimental results show that the forecasting accuracy of the proposed method is better than the method based on support vector machine and the method based on long and short-term memory network.

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Section: Experimental Verification and Results Analysismentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Short term heat load forecasting method based on deep belief network

Wang,

Zhao,

Ding

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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“…In view of the diversification of social electricity consumption structure and the complex environmental factors, the machine learning algorithm model prognosis means has become a study hot point of scholars in the field of electricity load forecasting. Among them, Artificial Neural Network (ANN) is a more popular forecasting method and it is a mathematical model established by imitating the nervous system in the human brain which can predict and learn unknown problems [11][12][13]. Recurrent Neural Networks (RNN) effectively break through the drawbacks that ANN cannot use the time-series dependencies between data to forecast by combining the timing of data with network design.…”

Section: Introductionmentioning

confidence: 99%

Short-term power load forecasting based on GRU neural network optimized by an improved sparrow search algorithm

Song

Cai

2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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Short-term power load forecasting is a very significant content in the operation and dispatch of power system, and it is a significant side to make certain the secure and economic operation of the power system and realize the scientific administration and dispatch of the power grid. By way of eliminating the matters of difficult parameter selection and insufficient forecasting accuracy in traditional forecasting methods, this paper use an improved sparrow search algorithm to optimize gated recurrent unit neural network. Firstly, Preprocess the raw load data.Secondly, use the processed data to train the model, and optimize model parameters with firefly sparrow search algorithm. Finally, carry out the power load forecast on the day to be forecasted, and comparative analysis with other two models, SSA-GRU and GRU , the results of the example indicate that the model established in this paper can advance the prognosis preciseness degree effectively and is effective in the application of short-term power load forecasting.

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Research on short-term power load forecasting based on Elman neural network with Genetic Algorithm

Cited by 2 publications

References 8 publications

Short term heat load forecasting method based on deep belief network

Short term heat load forecasting method based on deep belief network

Short-term power load forecasting based on GRU neural network optimized by an improved sparrow search algorithm

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