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Item HOURLY SOLAR RADIATION FORECASTING USING ARTIFICIAL NEURAL NETWORK MODEL FOR COLOMBO, SRI LANKA(Advances and Applications in Statistics, 2019) Saumyamala, M.G.A.; Chandrasekara, N.V.Sri Lanka is a tropical country located close to the equator with abundant sunlight throughout the year. For efficient utilization of this solar resource for power generation in photovoltaic (PV) systems and agricultural modelling, prior knowledge of global solar radiation (GSR) in the future is important. Limited availability of onsite GSR data and the high cost are the main barriers in forecasting GSR for Sri Lanka. As a solution this study suggests an artificial neural network (ANN) model to forecast hourly solar radiation using weather data and solar angles to forecast GSR in Colombo, specifically using feedforward neural network (FFNN) trained with Levenberg- Marquardt (LM) back propagation algorithm. Hourly weather data for 6 weather variables and two solar angles from 1st of March 2017 to 14th of February 2018 were used for training, validation and testing the network. Input parameters and training parameters were adjusted to identify the most accurate network configuration and the performance of the network was measured using normalized mean squared error (NMSE). Coefficient of determination (R2) measured to identify the appropriateness of using weather variables and solar angles to forecast solar radiation. The final hourly FFNN model consists of 2 hidden layers and there are 5 neurons and 3 neurons in each layer respectively. This model was able to forecast hourly solar radiation with 0.0961 NMSE and the R2 was 90.39%. This implies the capability of this model for prediction of global solar radiation when unseen weather data input supply to the model and ensure the accuracy of the result.Item Time series modeling of red onion production in Jaffna, Sri Lanka(Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Mirojan, U.; Varathan, N.; Arumairajan, S.Onion is one of the most important commercial vegetable crops grown in Sri Lanka. Observing fluctuation of onion production is essential in the market economy. The level of the production and the fluctuation not only has a significant influence on farmers and consumers, but also a reasonable effect on the safe running of the onion in market. In this study, the annual production of red onion in Jaffna is modeled by using Box – Jenkins time series approach. The Onion production in Jaffna is cultivated in two seasons, Maha season: from September to March, Yala season: from April to August. The annual seasonal red onion production data was obtained from the office of the Deputy Provincial Director of Agriculture (Extension) during the period of 1987 to 2016. The main objective of this study is to find the suitable Auto Regressive Integrated Moving Average (ARIMA) model for the annual production of Red onion in Jaffna. Further, three statistical criteria such as Akaike’s information criteria, Bayesian information criteria, mean squared error were carried out in order to select the best ARIMA model. Through the modeling, it was identified that ARIMA (1,1,0) is the best fitting model to the given data. Moreover, the model validation has been done using the actual figures. Further, the identified best model can be used to predict the red onion production of Jaffna in near future.Item Forecasting monthly household water consumption supplied by NWSD, Sri Lanka(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Perera, M.L.D.M.; Hewaarachchi, A.P.Water is an essential element for the survival of mankind and water supply is a pressing issue in this century. Household water use is generally the most important component of water consumption. In Sri Lanka, lack of freshwater has become a serious problem due to factors like population growth, overall expansion in economic activities, increased urbanization and changing climate patterns. Then as a country, managing water resources more efficiently has become a priority. This it is vital to forecast future monthly water consumption of households for planning purposes of further developments of the country. In this research, we aim to determine a suitable model for monthly household water consumption supplied by National Water Supply & Drainage Board (NWSDB), Sri Lanka in order to forecast future household water consumption. We consider monthly household water consumption data in Sri Lanka for the period from January 2005 to August 2016. The data shows an upward trend which suggests that the series is non-stationary. Also, data displays increasing variability and there’s a need to apply data transformation to stabilize the variance. Then, differencing techniques are applied to obtain a stationary series. Using Box-Jenkins methodology SARIMA (Seasonal Autoregressive Integrated Moving Average) model is identified as a reasonable model for the data. The result showed among several plausible ARIMA models, ARIMA (2, 1, 0) (1, 0, 1)12 model was appropriate for forecasting future values as it has the smallest AIC (Akaike information criterion) value. As a model validation technique, this model is then used to forecast last 5% of observations of data set. The accuracy of forecast error was assessed by mean percent error (MPE), mean absolute squared error (MASE) and mean absolute percent error (MAPE). The measures were 0.488, 0.287 and 2.213 respectively. As a future work it will be worthwhile to forecast water consumption for different regions. Also, to improve the accuracy of forecasts, models, which incorporate influential factors such as monthly precipitation, number of new connection will be considered.