Abstract
Due to the rapid pace of industrialization and growing demand for energy consumption, forecasting of renewable energy has become an inevitable focus of many recent studies. In this paper, our aim is to develop a univariate auto-regressive integrated moving average (ARIMA) model to forecast daily and monthly wind speed and temperature based on 15 years (2000–2014) of hourly data at Charanka Solar Park, Gujarat. To check the stationarity of time series, Dickey fuller test and rolling statistics plots are employed. Autocorrelation and partial autocorrelation plots are used to determine potential models, whereas Akaike information criterion (AIC) and Bayesian information criterion (BIC) are utilized to establish ARIMA (2, 1, 2) model. After rigorous training, model performance is validated using root means square (RMS) errors. The entire methodology is implemented in python using pandas for data exploration, and stats and scikit-learn libraries for model building and validation. On comparing results based on the log-likelihood, AIC and BIC values, we conclude that the ARIMA model provides better accuracy to the wind power forecasting as compared to solar power on the selected dataset.
Highlights
The renewable energy sources like solar, wind, geothermal, ocean and biomass energy provide clean and replenishable energy, principally different from fossil fuels in terms of their diversity, abundance and potential to withstand energy shortage issues faced by the developing economies (Rather 2018; Kumar et al 2010)
Our objective is to develop auto-regressive integrated moving average (ARIMA) models for wind speed and solar energy forecasting
Unlike traditional energy forecasting using probability distributions, here we develop a linear Autoregressive Integrated Moving Average (ARIMA) model that uses computer programming to provide reliable results with low computational complexity (Liu et al 2012; Shukur and Lee 2015)
Summary
The renewable energy sources like solar, wind, geothermal, ocean and biomass energy provide clean and replenishable energy, principally different from fossil fuels in terms of their diversity, abundance and potential to withstand energy shortage issues faced by the developing economies (Rather 2018; Kumar et al 2010). All, these renewable sources support a more sustainable future by producing neither greenhouse.
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