Abstract: This research was conducted in the Mae Sot
Watershed where located in the Moei River Basin at the Upper
Salween River Basin in Tak Province, Thailand. The Mae Sot
Municipality is the largest urban area in Tak Province and situated in
the midstream of the Mae Sot Watershed. It usually faces flash flood
problem after heavy rain due to poor flood management has been
reported since economic rapidly bloom up in recent years. Its
catchment can be classified as ungauged basin with lack of rainfall
data and no any stream gaging station was reported. It was attached
by most severely flood events in 2013 as the worst studied case for
all those communities in this municipality. Moreover, other problems
are also faced in this watershed, such shortage water supply for
domestic consumption and agriculture utilizations including a
deterioration of water quality and landslide as well. The research
aimed to increase capability building and strengthening the
participation of those local community leaders and related agencies to
conduct better water management in urban area was started by mean
of the data collection and illustration of the appropriated application
of some short period rainfall forecasting model as they aim for better
flood relief plan and management through the hydrologic model
system and river analysis system programs. The authors intended to
apply the global rainfall data via the integrated data viewer (IDV)
program from the Unidata with the aim for rainfall forecasting in a
short period of 7-10 days in advance during rainy season instead of
real time record. The IDV product can be present in an advance
period of rainfall with time step of 3-6 hours was introduced to the
communities. The result can be used as input data to the hydrologic
modeling system model (HEC-HMS) for synthesizing flood
hydrographs and use for flood forecasting as well. The authors
applied the river analysis system model (HEC-RAS) to present flood
flow behaviors in the reach of the Mae Sot stream via the downtown
of the Mae Sot City as flood extents as the water surface level at
every cross-sectional profiles of the stream. Both models of HMS and
RAS were tested in 2013 with observed rainfall and inflow-outflow
data from the Mae Sot Dam. The result of HMS showed fit to the
observed data at the dam and applied at upstream boundary discharge
to RAS in order to simulate flood extents and tested in the field, and
the result found satisfying. The product of rainfall from IDV was fair
while compared with observed data. However, it is an appropriate
tool to use in the ungauged catchment to use with flood hydrograph
and river analysis models for future efficient flood relief plan and
management.
Abstract: Load modeling is one of the central functions in
power systems operations. Electricity cannot be stored, which means
that for electric utility, the estimate of the future demand is necessary
in managing the production and purchasing in an economically
reasonable way. A majority of the recently reported approaches are
based on neural network. The attraction of the methods lies in the
assumption that neural networks are able to learn properties of the
load. However, the development of the methods is not finished, and
the lack of comparative results on different model variations is a
problem. This paper presents a new approach in order to predict the
Tunisia daily peak load. The proposed method employs a
computational intelligence scheme based on the Fuzzy neural
network (FNN) and support vector regression (SVR). Experimental
results obtained indicate that our proposed FNN-SVR technique gives
significantly good prediction accuracy compared to some classical
techniques.
Abstract: The purpose of this study is to forecast the influences
of information and communication technology (ICT) on the structural
changes of Japanese economies. In this study, input-output (IO) and
statistical approaches are used as analysis instruments. More
specifically, this study employs Leontief IO coefficients and
constrained multivariate regression (CMR) model in order to achieve
the purpose. The periods of initial and forecast in this study are 2005
and 2015, respectively. In this study, ICT is represented by ICT capital
stocks. This study conducts two levels of analysis, namely macro and
micro. The results of macro level analysis show that the dynamics of
Japanese economies on the forecast period, relative to the initial period,
are not so high. We focus on (1) commerce, (2) business services and
office supplies, and (3) personal services sectors when conducting the
analysis of the micro level. Further, we analyze its specific IO
coefficients when doing this analysis. The results of the analysis
explain that ICT gives a strong influence on the changes of these
coefficients from initial to forecast periods.
Abstract: The increasing demand of gallium, indium and
rare-earth elements for the production of electronics, e.g. solid
state-lighting, photovoltaics, integrated circuits, and liquid crystal
displays, will exceed the world-wide supply according to current
forecasts. Recycling systems to reclaim these materials are not yet in
place, which challenges the sustainability of these technologies. This
paper proposes a multispectral imaging system as a basis for a vision
based recognition system for valuable components of electronics
waste. Multispectral images intend to enhance the contrast of images
of printed circuit boards (single components, as well as labels) for
further analysis, such as optical character recognition and entire
printed circuit board recognition. The results show, that a higher
contrast is achieved in the near infrared compared to ultraviolett and
visible light.
Abstract: The rate of natural gas dissociation from the Coal
Matrix depends on depressurization of reservoir through removing of
the cleat water from the coal seam. These waters are similar to brine
and aged of very long years. For improving the connectivity through
fracking /fracturing, high pressure liquids are pumped off inside the
coal body. A significant quantity of accumulated water, a combined
mixture of cleat water and fracking fluids (back flow water) is
pumped out through gas well. In Queensland, Australia Coal Seam
Gas (CSG) industry is in booming state and estimated of 30,000 wells
would be active for CSG production forecasting life span of 30 years.
Integrated water management along with water softening programs is
practiced for subsequent treatment and later on discharge to nearby
surface water catchment. Water treatment is an important part of the
CSG industry. A case study on a CSG site and review on the test
results are discussed for assessing the Standards & Practices for
management of CSG by-product water and their subsequent disposal
activities. This study was directed toward (i) water management and
softening process in Spring Gully CSG field, (ii) Comparative
analysis on experimental study and standards and (iii) Disposal of the
treated water. This study also aimed for alternative usages and their
impact on vegetation, living species as well as long term effects.
Abstract: One of the major difficulties introduced with wind
power penetration is the inherent uncertainty in production originating
from uncertain wind conditions. This uncertainty impacts many
different aspects of power system operation, especially the balancing
power requirements. For this reason, in power system development
planing, it is necessary to evaluate the potential uncertainty in future
wind power generation. For this purpose, simulation models are
required, reproducing the performance of wind power forecasts.
This paper presents a wind power forecast error simulation models
which are based on the stochastic process simulation. Proposed
models capture the most important statistical parameters recognized
in wind power forecast error time series. Furthermore, two distinct
models are presented based on data availability. First model uses
wind speed measurements on potential or existing wind power plant
locations, while the seconds model uses statistical distribution of wind
speeds.
Abstract: Endowed of renewable energy sources (RES) are the
advantages of ASEAN, but they are using a low amount of RES only
to generate electricity because their primary energy sources are fossil
and coal. The cost of purchasing fossil and coal is cheaper now, but it
might be expensive soon, as it will be depleted sooner and after.
ASEAN showed that the RES are convenient to be implemented.
Some country in ASEAN has huge renewable energy sources
potential and use. The primary aim of this project is to assist ASEAN
countries in preparing the renewable energy and to guide the policies
for RES in the more upright direction. The Green-Y model will help
ASEAN government to study and forecast the economic concept,
including feed-in tariff.
Abstract: At present, the evaluation of voltage stability
assessment experiences sizeable anxiety in the safe operation of
power systems. This is due to the complications of a strain power
system. With the snowballing of power demand by the consumers
and also the restricted amount of power sources, therefore, the system
has to perform at its maximum proficiency. Consequently, the
noteworthy to discover the maximum ability boundary prior to
voltage collapse should be undertaken. A preliminary warning can be
perceived to evade the interruption of power system’s capacity. The
effectiveness of line voltage stability indices (LVSI) is differentiated
in this paper. The main purpose of the indices used is to predict the
proximity of voltage instability of the electric power system. On the
other hand, the indices are also able to decide the weakest load buses
which are close to voltage collapse in the power system. The line
stability indices are assessed using the IEEE 14 bus test system to
validate its practicability. Results demonstrated that the implemented
indices are practically relevant in predicting the manifestation of
voltage collapse in the system. Therefore, essential actions can be
taken to dodge the incident from arising.
Abstract: In this paper, we study the rainfall using a time series
for weather stations in Nakhon Ratchasima province in Thailand by
various statistical methods to enable us to analyse the behaviour of
rainfall in the study areas. Time-series analysis is an important tool in
modelling and forecasting rainfall. The ARIMA and Holt-Winter
models were built on the basis of exponential smoothing. All the
models proved to be adequate. Therefore it is possible to give
information that can help decision makers establish strategies for the
proper planning of agriculture, drainage systems and other water
resource applications in Nakhon Ratchasima province. We obtained
the best performance from forecasting with the ARIMA
Model(1,0,1)(1,0,1)12.
Abstract: Fuzzy systems have been successfully used for
exchange rate forecasting. However, fuzzy system is very confusing
and complex to be designed by an expert, as there is a large set of
parameters (fuzzy knowledge base) that must be selected, it is not a
simple task to select the appropriate fuzzy knowledge base for an
exchange rate forecasting. The researchers often look the effect of
fuzzy knowledge base on the performances of fuzzy system
forecasting. This paper proposes a genetic fuzzy predictor to forecast
the future value of daily US Dollar/Euro exchange rate time’s series.
A range of methodologies based on a set of fuzzy predictor’s which
allow the forecasting of the same time series, but with a different
fuzzy partition. Each fuzzy predictor is built from two stages, where
each stage is performed by a real genetic algorithm.
Abstract: Load Forecasting plays a key role in making today's
and future's Smart Energy Grids sustainable and reliable. Accurate
power consumption prediction allows utilities to organize in advance
their resources or to execute Demand Response strategies more
effectively, which enables several features such as higher
sustainability, better quality of service, and affordable electricity
tariffs. It is easy yet effective to apply Load Forecasting at larger
geographic scale, i.e. Smart Micro Grids, wherein the lower available
grid flexibility makes accurate prediction more critical in Demand
Response applications. This paper analyses the application of
short-term load forecasting in a concrete scenario, proposed within the
EU-funded GreenCom project, which collect load data from single
loads and households belonging to a Smart Micro Grid. Three
short-term load forecasting techniques, i.e. linear regression, artificial
neural networks, and radial basis function network, are considered,
compared, and evaluated through absolute forecast errors and training
time. The influence of weather conditions in Load Forecasting is also
evaluated. A new definition of Gain is introduced in this paper, which
innovatively serves as an indicator of short-term prediction
capabilities of time spam consistency. Two models, 24- and
1-hour-ahead forecasting, are built to comprehensively compare these
three techniques.
Abstract: This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study.
Abstract: Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. Interval Type 2 Fuzzy Logic System (IT2 FLS), with additional degrees of freedom, gives an excellent tool for handling uncertainties and it improved the prediction accuracy. The training data used in this study covers the period from January 1, 2012 to February 1, 2012 for winter season and the period from July 1, 2012 to August 1, 2012 for summer season. The actual load forecasting period starts from January 22, till 28, 2012 for winter model and from July 22 till 28, 2012 for summer model. The real data for Iraqi power system which belongs to the Ministry of Electricity.
Abstract: A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.
Abstract: In terms of ecology forecast effects of desertification, the purpose of this study is to develop a predictive model of growth and adaptation of species in arid environment and bioclimatic conditions. The impact of climate change and the desertification phenomena is the result of combined effects in magnitude and frequency of these phenomena. Like the data involved in the phytopathogenic process and bacteria growth in arid soil occur in an uncertain environment because of their complexity, it becomes necessary to have a suitable methodology for the analysis of these variables. The basic principles of fuzzy logic those are perfectly suited to this process. As input variables, we consider the physical parameters, soil type, bacteria nature, and plant species concerned. The result output variable is the adaptability of the species expressed by the growth rate or extinction. As a conclusion, we prevent the possible strategies for adaptation, with or without shifting areas of plantation and nature adequate vegetation.
Abstract: This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity.
The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.
Abstract: The work delineates the threats of maladjustment of the capacity of rain canals, designed and built in the early 20th century, in connection to heavy rainfall, especially in summer. This is the cause of the so called 'urban floods.' It directly relates to fierce raise of paving in the cities. Resolving this problem requires a change in philosophy of draining the rainfall by wider use of retention, infiltration and usage of rainwater.
In systemic approach to managing the safety of urban drainage systems the risk, which is directly connected to safety failures, has been accepted as a measure. The risk level defines the probability of occurrence of losses greater than the ones forecast for a given time frame. The procedure of risk modelling, enabling its numeric analysis by using appropriate weights, is a significant issue in this paper.
Abstract: Technical analysis comprised by various technical indicators is a holistic way of representing price movement of stocks in the market. Various forms of indicators have evolved from the primitive ones in the past decades. There have been many attempts to introduce volume as a major determinant to determine strong patterns in market forecasting. The law of demand defines the relationship between the volume and price. Most of the traders are familiar with the volume game. Including the time dimension to the law of demand provides a different visualization to the theory. While attempting the same, it was found that there are different thresholds in the market for different companies. These thresholds have a significant influence on the price. This article is an attempt in determining the thresholds for companies using the three dimensional graphs for optimizing the portfolios. It also emphasizes on the magnitude of importance of volumes as a key factor for determining of predicting strong price movements, bullish and bearish markets. It uses a comprehensive data set of major companies which form a major chunk of the Indian automotive sector and are thus used as an illustration.
Abstract: In the near future, companies will be increasingly forced to shift their activities along a new road in order to decrease the harmful effects of their design, production and after-life on our environment. Products must meet environmental standards to not only prevent penalties but to consider the sustainability for future generations. However, the most important factor that companies will face is selecting a reasonable strategy to maximize their profit. Thus, companies need to have precise forecast from their profit after design stage through Trade-off analysis. This paper is an attempt to introduce a mathematical model that considers effective factors that impact the total profit when products are designed for resource and energy efficiency or recyclability. The modification is according to different strategies based on a Cost-Volume-Profit model. Here, the cost structure consists of Recycling cost, Development cost, Ramp-up cost, Production cost, and Pollution cost. Also, the model shows the effect of implementation of design for recyclable on revenue structure through revenue of used parts and revenue of recycled materials. A numerical example is used to evaluate the proposed model. Results show that fulfillment of Green Product Development not only can reduce the environmental impact of products but also it will increase profit of company in long term.
Abstract: Third-party warehousing logistics has an important role in the development of external logistics. At present, the third-party logistics in our country is still a new industry, the accounting system has not yet been established, the current financial accounting system of third-party warehousing logistics is mainly in the traditional way of thinking, and only able to provide the total cost information of the entire enterprise during the accounting period, unable to reflect operating indirect cost information. In order to solve the problem of third-party logistics industry cost information distortion, improve the level of logistics cost management, the paper combines theoretical research and case analysis method to reflect cost allocation by building third-party logistics costing model using Time-Driven Activity-Based Costing(TDABC), and takes S company as an example to account and control the warehousing logistics cost.Based on the idea of “Products consume activities and activities consume resources”, TDABC put time into the main cost driver and use time-consuming equation resources assigned to cost objects. In S company, the objects focuses on three warehouse, engaged with warehousing and transportation (the second warehouse, transport point) service. These three warehouse respectively including five departments, Business Unit, Production Unit, Settlement Center, Security Department and Equipment Division, the activities in these departments are classified by in-out of storage forecast, in-out of storage or transit and safekeeping work. By computing capacity cost rate, building the time-consuming equation, the paper calculates the final operation cost so as to reveal the real cost.The numerical analysis results show that the TDABC can accurately reflect the cost allocation of service customers and reveal the spare capacity cost of resource center, verifies the feasibility and validity of TDABC in third-party logistics industry cost accounting. It inspires enterprises focus on customer relationship management and reduces idle cost to strengthen the cost management of third-party logistics enterprises.