Time series decomposition model for accurate wind speed forecast

Abstract

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.

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

“…For instance, Prema and Rao applied Holt-Winters, ARIMA and time series decomposition methods, and they found 28.63%, 23.26%, 18.24% MAPE', respectively. They also mentioned that 30% MAPE is acceptable by the Government of India [37]. We found MAPE for the time series decomposition, Holt-Winters and ARIMA methods at 19%, 14% and 12.9% respectively.…”

“…They found that the most significant part in the accuracy of forecasting is the seasonal decomposition method of the time series. Prema and Rao forecasted wind speed using time series decomposition, exponential smoothing and back propagation neural networks [37]. They observed that decomposition of time series and ARIMA methods gave more accurate results.…”

“…The time series has four different elements, which are the seasonal component (S), trend component (T), cycle component (C) and irregular component (E) [37,40,41]. The decomposition of time series (D) has two approaches, namely the additive and multiplicative model.…”

“…The decomposition of time series (D) has two approaches, namely the additive and multiplicative model. The multiplicative decomposition model multiplies the components one by another, whereas in the additive decomposition model component predictor variables are added to one another [37,[40][41][42][43]. In the model, trend component denotes long-term tendencies while cycle component indicates longer periodic seasonal movements.…”

“…In fact, the prediction has an impact on all data in descending order. In order to obtain the exponential smoothing equation, the previous prediction is added to the model with the alpha coefficient recursively and a new weighted equation is formed [37,[40][41][42][43][44][45][46].…”