Version 1
: Received: 6 May 2024 / Approved: 7 May 2024 / Online: 7 May 2024 (10:51:22 CEST)
How to cite:
Moyème, K.; Yao, B.; Sedzro, K. S.; Pidéname, T.; Yendoubé, L. Comparative Study of Electrical Loads Forecasting Based on Hybrid Approaches: Discrete Wavelet Transforms with Adaboost and LSTM. Preprints2024, 2024050376. https://doi.org/10.20944/preprints202405.0376.v1
Moyème, K.; Yao, B.; Sedzro, K. S.; Pidéname, T.; Yendoubé, L. Comparative Study of Electrical Loads Forecasting Based on Hybrid Approaches: Discrete Wavelet Transforms with Adaboost and LSTM. Preprints 2024, 2024050376. https://doi.org/10.20944/preprints202405.0376.v1
Moyème, K.; Yao, B.; Sedzro, K. S.; Pidéname, T.; Yendoubé, L. Comparative Study of Electrical Loads Forecasting Based on Hybrid Approaches: Discrete Wavelet Transforms with Adaboost and LSTM. Preprints2024, 2024050376. https://doi.org/10.20944/preprints202405.0376.v1
APA Style
Moyème, K., Yao, B., Sedzro, K. S., Pidéname, T., & Yendoubé, L. (2024). Comparative Study of Electrical Loads Forecasting Based on Hybrid Approaches: Discrete Wavelet Transforms with Adaboost and LSTM. Preprints. https://doi.org/10.20944/preprints202405.0376.v1
Chicago/Turabian Style
Moyème, K., Takouda Pidéname and Lare Yendoubé. 2024 "Comparative Study of Electrical Loads Forecasting Based on Hybrid Approaches: Discrete Wavelet Transforms with Adaboost and LSTM" Preprints. https://doi.org/10.20944/preprints202405.0376.v1
Abstract
The dynamic evolution and variation of electrical loads is now a priority for optimum management and, above all, forecasting. Indeed, these dynamic load variations require computer tools able to implement optimal load forecasting models. Scientific research into automated models for forecasting electrical loads therefore represents a challenge for scientific researchers. Several research studies have therefore been carried out. These include machine learning approaches such as: LSTM (Long Short-Term Memory), Support Vector Machine (SVM) and others. This article proposes a comparison study between a hybrid model based on the wavelet transform coupled with the adaboost method, and LSTM, for forecasting electricity consumption loads. Python 3.10 was used for the studies. The first results obtained consisted in the study of electricity load forecasts over four (04) data sets of the year (January, April, August and December). These results are very satisfactory, showing a good correlation between the real results of the electrical load consumption data, and the results of the implemented model. The electrical load consumption data are collected from a renewable energy production source: photovoltaic solar energy. The model test results (R2) obtained range from 0.919 to 0.958. These results are representative of real data and reflect the model's performance. The second set of results concerns a comparative study between the methods used by Y. Xie et al (CNN, MLP, LSTM-AM and LSTM-AM-MLP), and our approach. The prediction results obtained between the approaches show a prediction gap ranging from 0.15 to 0.78 for RMSE and 0.83 for MAPE. These results show significant differences between the methods. The minimization of errors by our model, therefore, reflects the model's performance in terms of accuracy, which is necessary for the optimal management of consumer electricity load forecasts in order to ensure balance between supply and demand.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
