Comparative Study of Time-Series Forecasting Models for Wind Power
Generation in Gujarat, India
Abstract
The rapid rate of transformation in the power sector of India has placed
a significant emphasis on robust grids and distributed generation units.
The observable shift in the energy sector, especially in wind and solar
energy, also requires smooth integration of Distributed Generation units
with the existing power grid. Precise wind power generation forecast,
therefore, becomes an important and complex task for the strategic
planning and management of the systems. We, thus, aim towards a system
that can actually provide precise wind power forecasts by applying
machine learning techniques. This work proposes a comparative and
comprehensive analysis of Artificial Neural Network (ANN), Recurrent
Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent
Unit (GRU), and Autoregressive Integrated Moving Average (ARIMA) model.
The experimentations and modelling are performed considering
meteorological and historical power generation data. The study is
concentrated in Kutch, Gujarat and is validated on the data collected
from the Central Electricity Authority (CEA), India for power generation
data and weather data collected from regional weather centres. The
findings show that ARIMA outperforms the other models for non-linear
data in multivariate analysis, with a MAPE score of 5.87 on the
prediction dataset.