This research aimed to design and construct a forecasting support system on demand of fresh durian of Thailand by applying 2 mathematical models which were Time Series model and Artificial Neural Network model in order to find an accurate model of forecasting support system on demand of durian yield to forecast durian yield quantity of Thailand in advance and use the forecast results to plan and determine quantity of fresh durian and durian product conforming with domestic consumption and export in order not to be over demand and cheap durian sold price. The forecast models were applied with Time Series model by using Moving Average Weighted Moving Average Single Exponential Smoothing and Holt’s Linear Exponential Smoothing techniques and Back Propagation of Artificial Neural Network model which used 4 input variables relating to durian yield. All of the forecast models were used to forecast total durian yield quantity in 26 provinces of durian cultivated area all around Thailand. Durian yield quantity data used was from 1996 to 2008. After that the forecast results were used to find the errors of MAE MSE RMSE and MAPE. Then the errors of each forecast model were compared. The forecast model which had the least error was the most accurate forecast model. The research result is following. Back Propagation with 4-8-1 structure of Artificial Neural Network model had the least error so that it was the most accurate forecast model to forecast fresh durian yield quantity in advance.

Thailand may experiencing The Dark World within 12 years in the future as the UK has revealed that could be found Crisis of Dark World after power not enough for The entire country in this Winter.[1] Thailand has to plan Thailands power capacity development within The National Energy Policy Committee (NEPC) The Cabinet in June 2007 assigned to the Ministry of Energy. Then Energy Policy and Planning Office (EPPO) and Electricity Generating Authority of Thailand (EGAT) have to plan Thailands power capacity development known as PDP 2007(Power Development Plan) Since 2007-2021 .Then first thing should take to forecast a shortage of power in Thailand. The Research found that Thailand may experiencing fuel shortages that will bring electricity such as Natural Gas in Gulf of Thailand will run out within 20 years Oil prices higher deterioration of the power plant about 5 plants. Current energy sources used to produce electricity to most of Thailand about. 66.19% of energy from natural gas remaining list is lignite approximately 12.60% coal 8.39% water energy 5.43% bought for 3.06% furnace oil 2.66% renewable energy 1.64% diesel oil 0.03% [2] The forecast demand of electricity since 2008-2021 is found since 2016 get start problem about Thailands capacity to produce electricity is 44562.8 MW but domestic demand is 44585.40 MW that mean capacity of energy is not enough for demand that lack approximately 295.6 MW. Thailand has to consider fuel source to bring electricity what be produced at lower costs to compete in international markets such as nuclear power plants etc.

The objective of this research is to design forecasting support models on demand of durian for domestic markets and export markets for durian gardeners durian entrepreneurs of domestic markets and export markets and the Office of Agricultural Economics to plan durian demand conforming with domestic markets and export markets because of Production and marketing main problems (The Agricultural Information Center The office of Agricultural Economics: 2008) such as over - much durian production during production capital increased farmers durian sold price tended to be lower and lower inefficient domestic market management and more export but cheaper price therefore after that to design forecasting for the purpose in order that durian would not be over demand which is containing factors of the demand of durian fresh durian frozen durian paste and durian chips for domestic markets and export markets exported to Asia America Australia and Africa. The research involves historical collection data in the period of 2002 to 2008. The basic of forecast models are the designed and improved models using an intelligent knowledge-based approach beginning Moving average Deseasonalized Exponential smoothing Double exponential smoothing and Artificial neural network (ANNs) program within the model of new value Creation and comparative accuracy models. The evaluation result of forecasting durian demand showed that lowest MAPE of Deseasonlized models at 3 month was durian fresh durian frozen and durian paste but durian chips showed that lowest MAPE of ANNs at 4 input 10 hidden layers and 1 output was high accurate forecasting and optimal models. The analysis recommendations and error forecast of durian demand are also presented.

This research focuses on the raw-material management such as fuel of a case study company being a freight forwarder in Chonburi province. The objective is to determine the economic order quantity (EOQ) and the reorder point (ROP) that minimize the total cost of handling. Five forecasting techniques are investigated to predict the future demand and it was found that the time series decomposition was the most suitable method with the least error. At the result of demand forecasting the economic order quantity is 16308.89 liters per time the total cost of handling the fuel tank is 17287.43 baht and there are ordering around 19 times per year. In addition the sensitivity study on demand factors affecting the cost of fuel tank management are also considered in this paper. It was found that 21% of the increased fuel demand brings the total handling cost is 1728.74 baht or increasing 10%. Therefore ordering economically can reduce the total cost of 1295.97 baht or 6.85%. If the company brings the studied results to improve the ordering process it will be able to forecast the order quantity more appropriately and effectively.

Weather forecasts are very necessary to predict the future weather conditions. The important thing in forecasting is the model and variables. The model used in the forecasting must be accurate and effective. In addition the variables used in forecasting must be comprehensive and appropriate to be able to represent the forecast. The initial conditions for forecasting must be appropriate and reliable. There are many methods to generate the initial conditions in forecasting. To generate the initial conditions for winter monsoon prediction with Kalman Filtering method which is a method used to predict the status of various systems and able to create values of variables used in forecasting to have a variety of similarities can be used as a starting variable in forecasting very well. In this research the winter monsoon prediction by the Shallow Water Model and validating the initial conditions by Root Mean Square Error (RMSE). The result of weather prediction in the winter monsoon can be used the Kalman Filtering method for generate the initial conditions.