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Academic Journal of Computing & Information Science, 2023, 6(11); doi: 10.25236/AJCIS.2023.061102.

Short-term Power Load Forecasting Based on a New Efficient Deep Learning Framework

Author(s)

Liguang Wang

Corresponding Author:
Liguang Wang
Affiliation(s)

State Grid Zhenjiang Power Supply Comphany, Jiangsu, China

Abstract

This research implemented a short-term power load forecasting model using a new deep learning framework called MindSpore. The new framework this research uses is more efficient than traditional deep learning frameworks such as TensorFlow. Firstly, the data is processed to meet the specific requirements of MindSpore. Subsequently, this research constructs the network architecture, comprising the LSTM layer, dropout layer, and fully connected layer. The effects of different parameters on the performance of the model are discussed in detail. The experiments unequivocally demonstrate the efficacy of the model in short-term power load forecasting.

Keywords

Power Load Forecasting, LSTM, Deep Learning Framework

Cite This Paper

Liguang Wang. Short-term Power Load Forecasting Based on a New Efficient Deep Learning Framework. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 11: 9-16. https://doi.org/10.25236/AJCIS.2023.061102.

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