Information and Management System for Renewable Energy Business

Information and Management System for Renewable Energy Business

DOI: 10.4018/978-1-5225-3625-3.ch006

Abstract

The basis of any analysis is done by the elements that are processed in an information system: data, information, knowledge, wisdom, highlighting the characteristics and differences between them. A higher level of organization and information processing involve analysis of information systems and management information system. There is a strong relationship between business architecture and software architecture, taking into account their interdependence and their parallel evolution in the business. SCADA, as technology for supervising and controlling industrial processes is presented through its characteristics and how it has contributed to the reorganization of the electricity system. The main objective of the chapter is to understand that the information processed in the REB cannot be treated chaotically, but should be treated properly, within a management information system, that allows processing of a huge volume of data in real time.
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Characteristics Of Information Used In Renewable Energy Business

The objective of this subchapter is to make a classification of the elements / information to be processed in the renewable energy business (REB), by describing their characteristics and the specific energy diagrams.

Any technical, economic or social system uses an elementary unit of information called data during normal functionality.

There are a lot of definitions of data, which depend on the user point of view.

A general approach, as occur in Collins dictionary is: “data means series of observation, measurements or facts”.

Data can be “information in raw or unorganized form (such as alphabets, numbers, or symbols) that refer to, or represent, conditions, ideas, or objects” (Luthra, 2016).

Wikipedia.org uses a general definition of data, as “a set of values of qualitative or quantitative variables [..] collected by a huge range of organizations and institutions, including businesses (e.g., sales data, revenue, profits, stock price), governments (e.g., crime rates, unemployment rates, literacy rates) and non-governmental organizations (e.g., censuses of the number of homeless people by non-profit organizations).

The definition of data is particularized for computing as “data is information that has been translated into a form that is more convenient to move or process. Data life cycle management (DLM) is a policy-based approach to managing the flow of an information system's data throughout its life cycle: from creation and initial storage to the time when it becomes obsolete and is deleted” (TechTarget network of Sites, 2017).

Webopedia, as online tech dictionary for IT, develop concept of data, taking into account data in a specific context such as that of database management systems, with data organized in data files; “data files are the files that store the database information, whereas other files, such as index files and data dictionaries, store administrative information, known as metadata”.

United For Sight web site contains a detailed analysis of data quality, both in terms of definition and characteristics.

Data quality is defined as “the totality of features and characteristics of a data set that bear on its ability to satisfy the needs that result from the intended use of the data” (United For Sight, 2017).

It is found that there are a lot of definitions of the word “data”, each of them surprising characteristic elements in the synthesis. Taking into account these elements, we can define the data as observations of the technical aspects of the energy processes that are taken over, converted to a standardized format, processed and transmitted as commands to the electronic devices that manage these processes.

There are a lot of attributes that are characteristic of high quality data. The appropriate set of attributes may differ depending on the purpose or direction of valorification of data, even many of these attributes are interdependent (Table 1).

Table 1.
Data quality
Characteristics of DataExplanation
Access securityData must be restricted and kept secure to ensure confidentiality
AccessibilityData must be available or easily retrievable.
AccuracyData must be correct and free of errors.
Appropriate amount of dataThe quantity of data must be appropriate.
BelievabilityData must be regarded as true and credible.
CompletenessData must be sufficient in breadth, depth, and scope for its desired use.
Concise representationData must be represented without being overwhelming.
Ease of UnderstandingData must be clear.
InterpretabilityData must be in appropriate language and units.
ObjectivityData must be unbiased.
RelevancyData must be applicable to the task at hand.
Representational consistencyData must be presented in a consistent format
ReputationData must come from a trusted source.
TimelinessData should be recorded as quickly as possible and used within a reasonable time period.
Value-addedData must provide valuable insight.

Source: United For Sight (2017).

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