Academic Journal of Engineering and Technology Science, 2022, 5(5); doi: 10.25236/AJETS.2022.050506.
Zhaozhengyang Li, Haoqian Wu
Automated Institute, Chongqing University of Posts and Telecommunications, Chongqing, China
In recent years, as the electrical energy of distributed storage has gradually increased, the randomness of load demand has increased. This makes it more difficult to rationally dispatch and store loads. How to quickly and accurately dig out more effective information and objective laws from the massive power load data, effectively clarify the instability and timing of load changes, and then reduce the consumption and accidents in dispatching is of great significance. This paper uses wavelet analysis and stochastic sparrow search algorithm to optimize LSTM neural networks to construct long- and short-term power load forecasting models, in Singapore as of December 2021 Empirical studies were conducted on the electricity load from 24 to January 23, 2022, as well as the corresponding peak-to-valley electricity prices, meteorological data and other related data. The results show that the long-term and short-term power load prediction model based on WNR-SSA-LSTM is compared with that of traditional LSTM In terms of neural networks, RMSE and MAPE were reduced by 4.000% and 1.7871%, model goodness-of-fit R2 increased by 0.0196, It is more suitable for short-term load forecasting.
Short- and long-term power load, Wavelet noise reduction, LSTM neural network, Sparrow search algorithm
Zhaozhengyang Li, Haoqian Wu. Short-term Power Load Forecasting Based on WNR-LSTM — Take the Singapore Region as an Example. Academic Journal of Engineering and Technology Science (2022) Vol. 5, Issue 5: 28-33. https://doi.org/10.25236/AJETS.2022.050506.
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