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- W4316659399 abstract "Nowadays, one of the fastest-growing renewable and sustainable energy sources is wind energy. Various statistical models require wind speed prediction for the calculation of wind energy, which in turn helps to calculate the annual energy to maintain the balance between electric power generation and consumption. Thus, time series forecasting is essential to sustain the balance. Mostly the wind data is not on the right scale as daily wind speed varies, resulting in a loss of model accuracy. Normalization eliminates this problem, but if the data provided is not adequately normalized, degeneration happens in deep learning models. Degeneracy and loss of precision can occur most of the time due to the lack of scaled raw data. Data with multiple features have different variations among all features. To overcome this problem, a basic neural layer can be formed to normalize the data. Precisely, a neural layer along learns to normalize data and adapts with the varying features, normalizing data more effectively and efficiently than the regular way of normalization. It is trained along with the model upon which it is applied. The proposed method uses the Min-Max normalization technique as a base for creating an adaptive neural layer for the model. This method is tested upon a Long-Short term Memory (LSTM) model with a multivariate wind speed dataset taken from NREL, and it is compared with traditional methods." @default.
- W4316659399 created "2023-01-17" @default.
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- W4316659399 date "2022-07-04" @default.
- W4316659399 modified "2023-09-27" @default.
- W4316659399 title "Forecasting Nonstationary Wind Data Using Adaptive Min-Max Normalization" @default.
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- W4316659399 doi "https://doi.org/10.1109/stpes54845.2022.10006473" @default.
- W4316659399 hasPublicationYear "2022" @default.
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