Download Research on China Energy Forecast and Early-Warning System

Research on China Energy Forecast and Early-Warning System
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Tian Zhiyong Guan Zhongliang
School of Economic and Management Beijing JiaoTong University Beijing 100044
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Abstract The paper researches the issue of integrated energy from problem analysis, database design
and the mechanism of forecast and early-warning. The research focuses on discussing such questions as
system structure, basic function and realization technology of China Energy Forecast and Early-Warning
System by the theory of Intelligence Decision Support System. It is a necessary preparation for creating
China Energy Forecast and Early-Warning System.
Keywords Intelligence decision support system(IDSS), energy, model, forecast, early-warning(EW),
component technology
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1. Introduction
Energy is the most important matter for social development, so every country in the world
emphasizes energy development very much. Most countries have created their energy inspection and
management systems. In 1993,the U.S completed its National Energy Model System(NEMS),and
updated it periodically. By this system, they issue the forecast information of Energy regularly.
In China, Energy has become more important than ever. In the National “11 Five-Year” plan, per
GDP energy-consuming has been provided definitely to be decreased by 20%.And energy development
must facilitate economic development. At the same time, the country’s environment deterioration also
constrains its energy development. All these have made the country to do much on analysis and research
on energy.
There are a lot of energy forecast researches in China that most of which focus on coil, oil and
electric power. Some have been applied in practice. But there is no a nationwide and comprehensive
energy forecast and early-warning system to analyze the current energy situation, to forecast the
energy production, consumption, efficiency and environmental protection etc, and to warn some crucial
energy situation early according to the requirement of the development period. And the system can not
only inspect the situation of energy executing, analyze the current energy situation and find problems,
but also support data for energy policy making. The system is very important for China. So we need
create the China Energy Forecast and Early-Warning System(CEFEWS) urgently.
2. The Basic Terms and Design Principles of CEFEWS Development
2.1 The Basic Terms of CEFEWS Development
2.1.1 Forecast/Early-Warning problem(solving problem) and Forecast/ Early-Warning solution(solving
solution)
A solving problem can be separated into a number of solving tasks(Figure 1).Each solving problem
is an atomic problem which can be solved only by a single algorithm. There are the subordination or
parallel relationships between each solving tasks. If one task ‘s completion depends on another one’s
completion, we call the latter is the sub-task of the former(the task D is sub-task of A in Figure 1). If
one task’s solving result is the solving problem’s result, we define the task as object task(As the task
A,B,C in Figure 1), otherwise we define it as middle task(As the task D in Figure 1). In CEFEWS
development, one solving solution consists of solving problems set and the relations between each tasks
which are gained by decomposing a solving problem. And we define the relations between each solving
task in a solving solution as solving path.
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T ask D
T a sk A
P r o b le m
se pa rate
T ask E
……
……
T ask B
T a sk C
F ig . 1 .
……
……
S o lu tio n
s e p a r a t i o n o f s o lv in g p r o b l e m
2.1.2 Forecast/Early-Warning model(solving model) and Forecast/Early-Warning algorithm(solving
algorithm)
Solving model is an application solving model built for solving tasks. Solving algorithm is
mathematical method used by a solving model. Solving algorithm is fixed generally, e.g. linear
regression algorithm. Solving model is changed with different tasks. Each solving model has one only
one solving algorithm, but one solving algorithm can be used by different solving models. For example,
solving model built according to liner regression algorithm can adopt either single or multiple variables.
In some cases, solving model is a mathematical formula or a statistical analysis process etc. In
other cases, solving model can be any decision-making activity that can be solved or simulated by one
paragraph computer program. Solving model can be divided into base model and composite model.
Method is the solving algorithm of solving model. Model is the pattern of solving some problems, but
method is the realization of specific processing. The relationship between solving mode and method is
one-to-many(1:N),i.e. one model can have many methods. Solving model is a separate computing unit
or data unit, consisted of model interface and realization module for the model.
2.2 The CEFEWS design principles of developing component model
The principles of developing component model are the separation between solving problem and
solving solution, the separation between solving task and solving model, the separation between solving
model and solving algorithm and the separation between solving model and solving data. That is that
different solving problems can have the same solving solution, different solving tasks can have the same
solving model, different solving model can have the same solving algorithm and the same solving
models can use different solving data. By these principles, the reusability of solving solution, solving
model and solving algorithm in CEFEWS component development and the modeling efficiency of
expert user can be improved remarkably.
3. CEFEWS System Structure And Function Introduction
3.1 designing thought
In nature, energy is the impetus for a country’s development. CEFEWS can provide national
leading departments with the current situation of energy by analyzing energy information from
multi-angle, multi-level. The system can provide future situation of energy by forecasting related
early-warning exponents by analyzing forecast indicators with various professional models according to
the actual situation of China. It can also provide warning intelligence, warning sources, analyze the
symptom and issue warning according to the energy emerging issues. By it, the national leading
departments can make policy to deal with the problem that may emerge in the future. As energy system
is complex and its components are quite different with different characteristics, we should adopt
professional models and algorithms according to its features. These models and algorithms are kept
changing with the environment and solving tasks so as to achieve optimal results. By adopting
component model development principle in CEFEWS design, it makes solving problem, solving
solution, solving model, solving algorithm and solving data independent. By the system, user can choose
early-warning exponents, forecast and early-warning model and algorithm according to the actual
situation. So the system can support user to judge the current and future situation of energy, and analyze
symptom until he found warning source intelligently and initiatively.
3.2 Database design
CEFEWS database system is a data warehouse system that contains different types and levels of
data. The system focuses on function title which has subject-oriented, integrated, and the temporal
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characteristics. It integrates macroeconomic database, conventional energy database, renewable energy
database, energy environment database, energy reserves database, energy supply database, energy
consumption database, the international energy databases and emphasis energy department database into
the unified workspace of CEFEWS. Comprising state and municipalities, departments, industries,
energy and other types of multi-level, multi-angle data in a comprehensive integration, the information
of the system is wide and plentiful. These data can be divided into basic data and structural data
according to their data features. Basic data is reported regularly by each sector or statistic data by each
statistics departments, e.g. all trades and region's GDP in macroeconomic database and energy
consumption values of all industries and all regions and industries in energy consumption database etc.
These data change frequently. Such data are the base of forecast and early-warning, defined as basic data.
Structural data ,e.g. administrative regions data, population data in macroeconomic database and data of
types of energy in conventional energy database, reflects the structure of administrative regions and
types of energy in tree ways. These data provide important basis for further analysis and multi-level,
multi-angle comprehensive energy information data mining. The relations between these data are shown
in Figure 2.
Basic data table
of demology
GDP data table
Region dimension
table
Basic data table
of energy
Energy type
dimension table
Industry
dimension table
Fig. 2. Energy data relation
In the figure, dimension tables reflect the attributes of structural data. The relations between
dimension table and basic data are the relations between energy system and other systems. It is a basic
relation figure. By a great deal of these relationships in the system, it forms the complex analysis of
energy information.
3.3 CEFEWS system structure
In essence, CEFEWS is an intelligence decision support system(IDSS). So it must utilize the
IDSS research achievement in designing the system. According to the design principle of IDSS and the
system requirements, the main elements of CEFEWS include intelligent man-machine interface(IMMI),
system general control module(SGCM), forecast system(FS), early-warning system(EWS), forecast
indicator management system(FIMS),early-warning indicator management system(EWIMS),
comprehensive energy information query system(CEIQS), energy information statistics and analysis
system(EISAS), and system result output system(SROS). Furthermore, there are three storage systems,
i.e. model base system, method base system and database system. Whole system framework structure is
as figure 3.
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User
IMMI
SGCM
FS
EWS
Model Base
System
FIMS
EWIMS
CEIQS
EISAS
SROS
Method Base
System
DBMS
Fig. 3. CEFEWS system structure
(1) intelligent man-machine interface(IMMI)
It is the window of the communication between system and user, including such functions as
menu choosing, question requirement, consultation explaining, graph displaying and result output etc.. It
can change user’s operation forms to the forms that system can understand, and display the result of
system in the form that user can understand easily.
(2) system general control module(SGCM
SGCM acts as control center in the system. It assists IMMI analyzing forecast, early-warning
problems etc., and also helps each modules or subsystem running.
(3) forecast system(FS)
FS is one of the core components. First, it chooses appropriate model from model base system
according to the forecast indicators created by FIMS. Second, it chooses appropriate methods from
method base system according to the model. Then, it chooses data from database as the parameters of
method, and computes forecast indicator.
(4) early-warning system(EWS)
EWS is another core component of the system. It is like FS. First, it chooses appropriate model
from model base system according to the early-warning indicators created by EWIMS. Second, it
chooses appropriate method from method base system according to the model. Then, it chooses data
from database as the parameters of method, processes the indicator by logic back to get the result.
Finally, it must issue the result in warning according to the early-warning indicator set up in the
early-warning setup database.
(5) forecast indicator management system(FIMS)
FIMS serves for FS, and also can be seen as part of the FS. Here, it is independent in order to
improve system’s maintainability and accessibility. The subsystem provides such functions as add,
delete and update of forecast indicator and the setup of indicator composing or their relations. It is easy
to setup indicator flexibly. It supports FS strongly and increases the openness of the system.
(6) early-warning indicator management system(EWIMS)
EWIMS serves for EWS. EWIMS is to EWS just as FIMS to FS. The difference is that EWIMS
need setup the relation between early-warning indicator and forecast indicator while managing
early-warning indicator and the supporting object is EWS.
(7) comprehensive energy information query system(CEIQS)
CEIQS is responsible for the inquiries of all sides of energy or some predetermined target,
including forecast indicator and early-warning data and the results etc.. It can provide query with
conditions flexibly, and display the query result in table, curve and histogram forms etc..
(8) energy information statistics and analysis system(EISAS)
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By analyzing energy information, EISAS can not only display properly the history and current
status of energy but also find the advantages and disadvantages by comparing with overseas. In terms of
energy, the system can do dimension analysis by data mining technology and find the rules or problems
behind phenomenon. If advantage, it supports the decision-makers to continue to maintain or further
improve. If disadvantage, it supports decision-makers to take appropriate measures to overcome.
(9) system result output system(SROS)
SROM can output the system result to screen or printer in table, curve graph or histogram forms
according to user’s requirement. The target is to display the system running result for decision-maker in
simple, visual and convenient forms.
(10) model base system
Model base system has an very important position in the system. Using CEFEWS, Users make
decision not directly on the data in database but on the models in the model base system. So from this
perspective, CEFEWS is “model-driven”. The results gained from model act three actions.
1) used to make decision directly
2) used to provide suggestion for decision making
3) used to simulate policy for estimating the consequences after implementing the decision
There are various models used by CEFEWS saved in the model base system. Centralized storage
management for the models is convenient for being reused and maintained.
(11) method base system
Method base is further decomposition for the model library. One model may need many methods
as mentioned above, so model-method separation further increases the maintainability of the system and
reduces the workload of system development.
(12) database system
DBMS can define database structure, operate DB, control data security and integrity, maintain and
restore DB etc.. CEFEWS database system is a data warehouse, including a wide range of data. It is the
basis of system.
4. Forecast Analysis
Forecast is the core of system. It predicts the future energy situation on the basis of historical data.
At the same time, it is the basis of early-warning. First, it need setup forecast indicator. Then it adopts
appropriate model to predict. There are two types of forecast indicators in CEFEWS. One is basic
indicator(atomic indicator), which indicates some fundamental data, e.g. coil, oil and other energy
consumption of a region. The other is composite indicator, which is gained by other indicators(basic or
other composite indicators) according to some mathematical or logical relationship. These indicators are
results of preliminary analysis on basic indicators. It can reflect a certain aspect of energy system. Some
have eliminated dimension, so they can be used for other regions and countries as comparable basis. For
instance, the proportion of coal consumption indicator is the ratio of coal consumption in the region and
total energy consumption.
5. Early-Warning Analysis
Early-Warning(EW) is advanced application on the basis of forecast. There are three ways from
forecast indicator to EW indicator. 1) Make full use of forecast indicators according to a certain logic,
mathematics relations. 2)Make part use of forecast indicators and some indicators defined by specialists
according to a certain model relationships. 3)Make full use of indicators defined and processed by
specialists.
These three ways reflect three types of EW problems. 1) The first type reflects some well
structured EW problems, which can be solved using relevant forecast indicators by appropriate models.
2) The second is not well structured EW problem, i.e. semi-structured. It depends on not only forecast
indicators but also indicators defined by specialists and processed according to appropriate models. 3)
The third is non-structured EW problem. For example, the problem of some events’ effects on energy is
only dependent on specialists’ knowledge and experience for it is very hard to be quantified.
In system, there are different system structures to be realized according to the three types of
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problems. 1) It uses the model and method base system as designed above to realize. 2) In addition to
model and method base system, it sets high requirements for IMMI. It needs IMMI and the knowledge
base’s support when necessary which can partially or completely replace specialists. 3) Apart from the
model and method, it depends on IMMI and knowledge base more. It needs an active learning
system(ALS) with which IMMI can support specialist system better. ALS can learn specialist’s
experience and knowledge, and keep them in the knowledge base. The problem can be solved by
specialist and knowledge base even only by knowledge base.
Each EW problem and its solution in system are shown in the following figure.
M ode l b ase
EW S
M ethod b ase
E W se tup b ase
Fore cast indica tor b ase
F ig. 4. S tru ctu re E W p rob lem
IM M I
EW S
E W s e tu p
base
K n o w le d g e
base
M odel
base
M e th o d b a s e
F o re c a s t in d ic a to r b a s e
F ig . 5 . S e m i-s tr u c tu r e d E W p r o b le m
IMMI
EWS
Knowledge
base
ALS
EW setup base
Fig. 6. non-structured EW problem
6. System Technology
Component technology is drawn from software reuse. Software reuse is using existing software
components to construct new software. It not only reduces the cost and time of software development
but also can improve the maintainability and reliability of software. After Microsoft introduced in
technical specification COM~COM , SUN introduced Java Bean/EJB, and HP, 3 COM, Canon and
other companies and organizations have also launched CORBA. These are the main three technology
specifications of component technology. In each specifications, component has packaging, reusability,
independence, messaging and expandability.
It can be found from CEFEWS system structure mentioned above that this system is very suitable
for component technology to achieve. CEFEWS model is a computer program(service) that can solve
decision problem independently. It can compose a composite model with other models to solve more
complex decision problems. All these require that CEFEWS models should have relative independence
and reusability which is provided by component model. In CEFEWS architecture, model plays a leading
role. It drives other parts to complete decision-making function. So it is up to business logic in
multi-layer structure. From the perspective of achieving, model’s operations, such as activation,
concurrence, synchronization, choice and iteration etc, can be realized by relevant model link. So, it is
feasible to introduce component technology in CEFEWS.
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7. Conclusion
For the complexity and data management dispersibility of energy problem, now there is no an
integrated energy information system to analyze basic energy data and provide quantificational support
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for decision maker in China. With forecast, EW and analysis models, CEFEWS can support user
quantificationally to master energy current situation, future trend and to do energy policy simulation etc..
So, the paper studies CEFEWS from database, system structure, system development principle and
forecast and EW analysis etc.. It is the foundation of integrated energy research and CEFEWS
realization by computer.
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