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Wind speed forecasting is essential for various domains, such as renewable energy generation, aviation, agriculture, and disaster management. However, wind speed is a complex and stochastic phenomenon that exhibits multiple stochastic patterns. Therefore, forecasting methods that can account for the dynamics and uncertainty of wind speed are required. The literature on wind forecasting classifies the time-scale into different categories, such as short-term, medium-term, and long-term. The data used for these categories is usually daily or sub-daily, such as hourly. This implies that high frequency data poses more challenges for the modeling. Machine learning (ML) methods have shown great potential in forecasting wind speed. However, ML methods may not be able to capture the complex and stochastic patterns of time series data, especially when there are multiple seasonal cycles. Therefore, it is often beneficial to use a preprocessing method like time series decomposition before applying ML methods. This article proposes a novel hybrid model that integrates machine learning and time series decomposition for forecasting wind speed. A multivariate seasonal trend decomposition method based on loess (MSTL) is used to preprocess the wind speed data before applying various forecasting methods. MSTL is a generalization of the popular STL method that can split the time series data into a trend component, multiple seasonal components, and a residual component. The seasonal components are considered as deterministic and forecasted using a seasonal naive method that repeats the most recent cycle. For the other non-seasonal components, the neural network autoregression (NNAR) method is employed. The same approach is followed for two common statistical methods: autoregressive integrated moving average and exponential smoothing state space model (ETS). This enables the comparison of the performance of machine learning and statistical forecasting methods based on time series decomposition. The MSTL-NNAR model is also contrasted with the NNAR model without preprocessing to assess the effectiveness of MSTL. The proposed model is applied to two wind speed datasets with different frequencies: daily and hourly. The model is evaluated for both long-term and short-term forecasts by comparing it with other models using various accuracy and bias measures. The results show that the hybrid model, MSTL-NNAR, outperforms all other models in terms of accuracy and bias for both types of data and both time horizons. The results also show that MSTL preprocessing significantly improves the performance of NNAR by reducing its error and bias. The results demonstrate the effectiveness of the hybrid model for forecasting wind speed data that are complex, nonlinear and high-frequency. The model can provide reliable forecasts for different time horizons and different types of data. The model can be useful for various applications, such as wind power generation, weather prediction, and environmental monitoring. [ABSTRACT FROM AUTHOR]

Long term water demand forecasting is needed for the efficient planning and management of water supply systems. A Monte Carlo simulation approach is adopted in this paper to quantify the uncertainties in long term water demand prediction due to the stochastic nature of predictor variables and their correlation structures. Three future climatic scenarios (A1B, A2 and B1) and four different levels of water restrictions are considered in the demand forecasting for single and multiple dwelling residential sectors in the Blue Mountains region, Australia. It is found that future water demand in 2040 would rise by 2 to 33 % (median rise by 11 %) and 72 to 94 % (median rise by 84 %) for the single and multiple dwelling residential sectors, respectively under different climatic and water restriction scenarios in comparison to water demand in 2010 (base year). The uncertainty band for single dwelling residential sector is found to be 0.3 to 0.4 GL/year, which represent 11 to 13 % variation around the median forecasted demand. It is found that the increase in future water demand is not notably affected by the projected climatic conditions but by the increase in the dwelling numbers in future i.e. the increase in total population. The modelling approach presented in this paper can provide realistic scenarios of forecasted water demands which would assist water authorities in devising appropriate management strategies to enhance the resilience of the water supply systems. The developed method can be adapted to other water supply systems in Australia and other countries. [ABSTRACT FROM AUTHOR]