Research on Short-Term Forecast of Power Load Based on Adam-Gru

Jia, Pengtao; Miao, Yunfeng

doi:10.1088/1742-6596/1693/1/012116

Cited by 4 publications

References 3 publications

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Short-term power load forecasting based on I-GWO-KELM algorithm

Chen

Dong

Shi³

2021

MATEC Web Conf.

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In this paper, I-GWO-KELM algorithm is used for short-term power load forecasting. Normalize the power data and meteorological data of the short-term power load, and use GWO to optimize the regularization coefficient of KELM and the RBF kernel parameters. To apply the model to short-term power load forecasting to obtain simulations for the next 24 hours and 168 hours curve. Experiments show that the improved model I3-GWO-KELM proposed in this paper has the best effect. The improvement of GWO in this paper is effective and feasible. In the application of short-term power load forecasting, the IGWO-KELM model is more accurate than the ELM and KELM models.

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Short-term power load forecasting based on I-GWO-KELM algorithm

Chen

Dong

Shi³

2021

MATEC Web Conf.

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Short-Term Three-Phase Load Prediction With Advanced Metering Infrastructure Data in Smart Solar Microgrid Based Convolution Neural Network Bidirectional Gated Recurrent Unit

et al. 2022

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The collaboration development between the power supply, solar power, and storage batteries is a formidable commission in the smart solar microgrid. The efficient power management integrated into a smart solar microgrid is required to address these problems. The deep learning predictive tools are utilized to forecast the maximum building's three-phase load power in the short term. In this project, historical data, from the solar module, battery module, grid data, and climate data, are collected by integrated advanced metering infrastructure in a smart solar microgrid in Taiwan. These historical data are utilized in predicting the building's three-phase load power in the smart solar microgrid. This project proposed the hybrid model in the short-term three-phase loads forecasting based on convolution neural network bidirectional gated recurrent unit (CNN-Bi-GRU). The CNN-Bi-GRU utilizes the continuous-time sliding window, which is extracted features and reshaped into vectors by CNN layers. The hyperparameter optimization is utilized to construct the highest performance structure of the CNN-Bi-GRU model. The CNN-Bi-GRU performance is compared and evaluated with other state-of-arts models, which are the recurrent neural network (RNN), LSTM, gated recurrent unit (GRU), bidirectional LSTM (Bi-LSTM), and Bi-GRU models. The experiment results prove that the forecasting accuracy could be effectively improved by a hybrid CNN-Bi-GRU model with an appropriate window size of sequential historical data. According to research knowledge, this is the first work to predict the building's three-phase load power using multiple source data from advanced metering infrastructures (AMI) and deep learning CNN-Bi-GRU in the smart solar microgrid. The CNN-Bi-GRU model is successfully integrated into the efficient power management, which maintains the adequate battery storage level, solar power, and grid power balance under different weather conditions and working requirements of the building's three-phase load power.INDEX TERMS Building's three-phase load power prediction, Convolution neural network Bidirectional gated recurrent unit, deep learning machine, gated recurrent unit, short-term load power, long short-term memory, recurrent neural network.

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