Time series decomposition model for accurate wind speed forecast

Climate change can be considered to be the greatest environmental challenge our world is facing today. Along with the need to ensure long-term assurance of energy supply, it imposes an obligation on all of us to consider ways of reducing our carbon footprint and sourcing more of our energy from renewable sources. Wind energy is one such source and forecasting methods for the prediction of wind speed are becoming increasingly significant due to the penetration of wind power as an alternative to conventional energy sources. This paper proposes time series models for short-term prediction of wind speed. The predictions are done for 1 day ahead using different time series models. For each model, these predicted values are compared with the actual values for the next day. Basic exponential smoothing for different duration of data was tested. A hybrid model with decomposition and exponential smoothing is proposed. A multiplicative decomposition is carried out for the measured data. Separate models were developed for seasonal and trend series and then combined to carry out the forecast. The models were tested for different durations of samples and different weather conditions. It is observed from the results that the prediction with decomposition model for 4 months data gave the least error.