In the global energy context, renewable energy sources such as wind is considered as a credible candidate for meeting new energy demands and partly substituting fossil fuels. Modelling and forecasting wind speed are noteworthy to predict the potential location for wind power generation. An accurate forecasting of wind speed will improve the value of renewable energy by enhancing the reliability of this natural resource. In this paper, the wind speed data from year 1990 to 2014 in 18 meteorological stations throughout Peninsular Malaysia were modelled using the Autoregressive Integrated Moving Average (ARIMA) to forecast future wind speed series. The Ljung-Box test was used to determine the presence of serial autocorrelation, while the Engle’s Lagrange Multiplier (LM) test was used to investigate the presence of Autoregressive Conditional Heteroscedasticity (ARCH) effect in the residual of the ARIMA model. In this study, three stations showed good fit using the ARIMA modelling since no serial correlation and ARCH effect were present in the residuals of the ARIMA model, while the ARIMA-GARCH had proven to precisely capture the nonlinear characteristic of the wind speed daily series for the remaining stations. The forecasting accuracy measure used was based on the value of root mean square error (RMSE) and mean absolute percentage error (MAPE). Both ARIMA and ARIMA-GARCH model proposed provided good forecast accuracy measure of wind speed series in Peninsular Malaysia. These results will help in providing a quantitative measure of wind energy available in the potential location for renewable energy conversion.

Four pollution related diseases, namely asthma, conjunctivitis, URTI and dengue will be studied in terms of their trend, behaviour and association with influential factors such as air pollution and climate variables. Two methods were chosen; Poisson Generalized Linear Model and Negative Binomial Model. These methods were used to determine the association between the diseases and their influential factors. This study shows that Sulphur Dioxide (SO2) is the most abundant source that contributes to the diseases. Therefore, the local authorities such as the Department of Environment need to reinforce the law in planning and monitoring the SO2 sources which are produced from fuel combustion in mobile sources and motor vehicles.

Abstract. Wind speed is a fundamental atmospheric variable which plays an important role in energy production industry. Wind speed affects weather forecasting, aircraft and maritime operations, and other numerous effects. As a rising tenders of wind energy, it is vital for power efficacies to plan the adaptation of wind power. Henceforth, an accurate measurement of wind speed prediction is ideal for providing an impression on how the behavior and trend of historical wind pattern and future projected pattern could be. Wind speed forecasting is important for the reliable and efficient operation of the wind power system. The ability of Fourier based-ARMA model was tested to forecast the wind speed data of 3 crucial locations; Senai, Bayan Lepas and Subang. A comparison has been made between conventional method and Fourier analysis method. Fourier-ARMA was found to outperform the conventional ARMA model in predicting the wind speed data for 365 days ahead with just a little error.

Johor Bahru with its rapid development where pollution is an issue that needs to be considered because it has contributed to the number of asthma cases in this area. Therefore, the goal of this study is to investigate the behaviour of asthma disease in Johor Bahru by count analysis approach namely; Poisson Integer Generalized Autoregressive Conditional Heteroscedasticity (Poisson-INGARCH) and Negative Binomial INGARCH (NB-INGARCH) with identity and log link function. Intervention analysis was conducted since the outbreak in the asthma data for the period of July 2012 to July 2013. This occurs perhaps due to the extremely bad haze in Johor Bahru from Indonesian fires. The estimation of the parameter will be done by quasi-maximum likelihood estimation. Model assessment was evaluated from the Pearson residuals, cumulative periodogram, the probability integral transform (PIT) histogram, log-likelihood value, Akaike’s Information Criterion (AIC) and Bayesian information criterion (BIC). Our result shows that NB-INGARCH with identity and log link function is adequate in representing the asthma data with uncorrelated Pearson residuals, higher in log likelihood, the PIT exhibits normality yet the lowest AIC and BIC. However, in terms of forecasting accuracy, NB-INGARCH with identity link function performed better with the smaller RMSE (8.54) for the sample data. Therefore, NB-INGARCH with identity link function can be applied as the prediction model for asthma disease in Johor Bahru. Ideally, this outcome can assist the Department of Health in executing counteractive action and early planning to curb asthma diseases in Johor Bahru.

The relative humidity (RH) of 13 stations all over the peninsular Malaysia for the period of 1968 to 2009 is examined in this study. In understanding the trend flow, the Mann-Kendall (MK) trend test of RH of selected 13 stations all over Malaysia reported a decreasing trend over all parts excluding one station. RH prediction is an important problem in the climate change study; it determines future trend based on past values. The main goal of this paper is to create a model and make future trend predictions using RH data. Among the most effective and prominent approaches for analysing time series data is the methods introduced by Box and Jenkins. In this study we applied the Box-Jenkins methodology to build an RH-Seasonal Autoregressive Integrated Moving Average model (SARIMA) for monthly RH data. The RH-SARIMA model for each station was developed. These models were used to forecast 30 months upcoming RH data. The result will help decision makers to establish priorities in terms of climate change impact over peninsular Malaysia.