Short-Term Multiple Load Forecasting Model of Regional Integrated Energy System Based on QWGRU-MTL

Wang, Songyao; Zhang, Zhisheng

doi:10.3390/en14206555

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…Energies 2023, 16, 3697 3 of 13 [22] constructed a gas outburst risk grade prediction model based on an optimized quantum gated-node neural network. In recent years, the quantum weighted neural network, which is a common form of quantum neural networks, has been applied in the field of multiple load short-term forecasting [23].…”

Section: Quantum Weighted Neuronmentioning

confidence: 99%

Research on Multiple Load Short-Term Forecasting Model of Integrated Energy Distribution System Based on Mogrifier-Quantum Weighted MELSTM

Song

Zhang

2023

Energies

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Accurate and efficient short-term forecasting of multiple loads is of great significance to the operation control and scheduling of integrated energy distribution systems. In order to improve the effect of load forecasting, a mogrifier-quantum weighted memory enhancement long short-term memory (Mogrifier-QWMELSTM) neural network forecasting model is proposed. Compared with the conventional LSTM neural network model, the model proposed in this paper has three improvements in model structure and model composition. First, the mogrifier is added to make the data fully interact with each other. This addition can help enhance the correlation between the front and rear data and improve generalization, which is the main disadvantage of LSTM neural network. Second, the memory enhancement mechanism is added on the forget gate to realize the extraction and recovery of forgotten information. The addition can help improve the gradient transmission ability in the learning process of the neural network, make the neural network remain sensitive to distant data information, and enhance the memory ability. Third, the model is composed of quantum weighted neurons. Compared with conventional neurons, quantum weighted neurons have significant advantages in nonlinear data processing and parallel computing, which help to improve the accuracy of load forecasting. The simulation results show that the weighted mean accuracy of the proposed model can reach more than 97.5% in summer and winter. Moreover, the proposed model has good forecasting effect on seven typical days in winter, which shows that the model has good stability.

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Section: Quantum Weighted Neuronmentioning

confidence: 99%

Research on Multiple Load Short-Term Forecasting Model of Integrated Energy Distribution System Based on Mogrifier-Quantum Weighted MELSTM

Song

Zhang

2023

Energies

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“…Due to the complex reasons in the measurement device itself or in the data transmission process, the original data usually has faws, which will greatly afect the accuracy of the prediction. Terefore, in order to improve the data quality, [30] preprocessed the original data.…”

Section: 1mentioning

confidence: 99%

Power Load Prediction Based on IGWO‐BILSTM Network

Wang

Liu

2023

Mathematical Problems in Engineering

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Recently, neural networks based on intelligent algorithms have been widely used in short-term power load prediction. However, these algorithms have poor reproducibility in the case of repetition. Aiming at the shortcomings of the gray wolf optimizer (GWO) algorithm, such as slow convergence speed and easy to fall into local optimum, an improved gray wolf optimizer (IGWO) was proposed. In order to better extract features from time series data, the improved grey wolf optimizer-bidirectional long short-term memory (IGWO-BILSTM) power load prediction model is established by bidirectional long short-term memory (BILSTM). Pearson correlation analysis uses the actual power load data set of a certain region and selects the highly correlated factors as the input of the network. The BILSTM model was used to train the preprocessed data, the IGWO algorithm was used to find the optimal solution parameters during the training, and finally, the optimal prediction results were output to the test set. The experimental results show that compared with the prediction model established by the traditional optimization algorithm, the mean absolute percentage error (MAPE) value of the IGWO-BILSTM model is 1.63, the root mean square error (RMSE) value is 1.47, and the mean absolute error (MAE) value is 1.17. The convergence speed is faster and the prediction result is more accurate. The accuracy of multidimension load forecasting is greatly improved.

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“…It helps in managing immediate supply and demand challenges which has made it attract attention. Over the past thirty years, multiple models have been utilized in various methods for short-term load forecasting such as neural network models [5][6][7][8], fuzzy logic [9,10], fuzzy-neural network structures [11,12], regression-based and load profiling-based methods [13,14], and expert system techniques [15,16].…”

Section: Introductionmentioning

confidence: 99%

Bottom-Up Medium-Term Hourly Resolution Household Electricity Load Profile Model

Sayed

2024

Preprint

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Electricity load profiling at the household level is essential for Demand-side management, and Electrical systems designing, Where having reliable electricity is crucial and required. This paper introduces a load profile model that generates hourly load profiles for the entire year. This model is aimed at filling gaps in understanding household electricity use and provides valuable insights into electricity demand modeling. Our research is based on a detailed survey that included household socioeconomics, electricity usage, appliance inventory, and behavior patterns. In this study, We utilized the gathered data to classify households into three distinct socioeconomic groups. This categorization was based on their income levels and lifestyles and developed a probabilistic bottom-up approach to create customized electricity appliance profiles. This approach gives accurate power consumption behavior estimates. Our method integrates probabilistic appliance usage with the impact of socioeconomic and environmental factors like ambient temperature on energy usage. finally validates the result against actual consumption reports. also, We compared our Load Profile Generation (LPG) model with previously estimated load profiles. This comparison highlighted that variations in household power usage, influenced by human behavior, add complexity to the load profile, even when daily energy needs at home are similar.

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Short-Term Multiple Load Forecasting Model of Regional Integrated Energy System Based on QWGRU-MTL

Cited by 10 publications

References 10 publications

Research on Multiple Load Short-Term Forecasting Model of Integrated Energy Distribution System Based on Mogrifier-Quantum Weighted MELSTM

Research on Multiple Load Short-Term Forecasting Model of Integrated Energy Distribution System Based on Mogrifier-Quantum Weighted MELSTM

Power Load Prediction Based on IGWO‐BILSTM Network

Bottom-Up Medium-Term Hourly Resolution Household Electricity Load Profile Model

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