scispace - formally typeset
Proceedings ArticleDOI: 10.1109/ISCO.2013.6481117

Multicriteria decision examination for electrical power grid monitoring system

21 Mar 2013-pp 26-30
Abstract: Rough Set theory proposed by Z Pawlak is a great help for dealing with uncertain data and dominance based rough set is an extension to the classical rough set theory considering the preference order of the data. As because of the lot number of variables and high degree of uncertainty, the process of a grid's power monitoring and its system is quite complicated. In this paper, we have used dominance based rough set approach for investigation of electrical grid monitoring system, based upon which a grid operator will be able to take efficient and intelligent decisions.

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Topics: Dominance-based rough set approach (71%), Rough set (63%), Electrical grid (56%) ...read more
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Open accessProceedings ArticleDOI: 10.1109/ICMIRA.2013.79
01 Dec 2013-
Abstract: High-speed, accuracy, meticulousness and quick responses are the notion of the vital necessities for modern digital world. An efficient electronic circuit unswervingly affects the maneuver of the whole system. Different tools are required to unravel different types of engineering tribulations. Improving the efficiency, accuracy and low power consumption in an electronic circuit is always been a bottle neck problem. So the need of circuit miniaturization is always there. It saves a lot of time and power while switching of gates and reduces the wiring-crises. Therefore to trounce with this problem we have proposed an artificial intelligence (AI) based approach that makes use of Rough Set Theory for its implementation. Theory of rough set has been proposed by Z Pawlak in the year 1982. Rough set theory is a new mathematical tool which deals with uncertainty and vagueness. Decisions can be generated using rough set theory by reducing the unwanted and superfluous data. We have condensed the number of gates without upsetting the productivity of the given circuit. This paper proposes an approach using artificial intelligence technique with the help of rough set theory which basically lessens the number of gates in the circuit, based on decision rules.

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Topics: Miniaturization (61%), Rough set (53%)

1 Citations


Proceedings ArticleDOI: 10.1109/ICROIT.2014.6798320
01 Feb 2014-
Abstract: The role of wireless sensor networks (WSNs) is essential to the present scenario. Numerous independent sensors exist in a WSN that cover the entire terrain and have the task of inspecting a specific phenomenon. For this reason, the sensors must work with minimal constraints, such as least energy consumption and data memory. To achieve the objective, many techniques are applied, one of which is clustering, where nodes are congregated into clusters and a head node is elected. Suitable cluster head selection is crucial for improving the energy management of a sensor network. In this paper, a technique based on a dominance based rough set is introduced for cluster head selection on the basis of attributes that have the capacity to augment the sustainability and lifetime of the sensor network. This method is far superior to the probabilistic approach of cluster head selection in a WSN.

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Book ChapterDOI: 10.1016/B978-0-12-817772-3.00016-1
Anshuman Dey Kirty1Institutions (1)
01 Jan 2021-
Abstract: The chapter provides an overview of the trends in consumption of energy and its source: renewable and nonrenewable across the globe with a focus on the developing nations. The study has been made for the last two decades. As the developing nations require energy to power their growth; this case study has shown how fast the consumption of the energy takes place and their projections until the year 2026. Almost 59% of the energy productions are from oil and coal for decades. The that what we have expected. With the increase in the usage of energy, we break down each major developing nation and study its energy profile. This chapter also further proposes time series analysis to project usage in the coming years. The forecast in this chapter has been accomplished using the open-source library published by Facebook for time series projections, namely Prophet. It has taken the seasonality and the yearly trends into account as parameters and therefore has given a satisfactory result on the projections.

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Topics: Energy consumption (58%)
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Journal ArticleDOI: 10.1016/S0377-2217(00)00167-3
Abstract: The original rough set approach proved to be very useful in dealing with inconsistency problems following from information granulation. It operates on a data table composed of a set U of objects (actions) described by a set Q of attributes. Its basic notions are: indiscernibility relation on U, lower and upper approximation of either a subset or a partition of U, dependence and reduction of attributes from Q, and decision rules derived from lower approximations and boundaries of subsets identified with decision classes. The original rough set idea is failing, however, when preference-orders of attribute domains (criteria) are to be taken into account. Precisely, it cannot handle inconsistencies following from violation of the dominance principle. This inconsistency is characteristic for preferential information used in multicriteria decision analysis (MCDA) problems, like sorting, choice or ranking. In order to deal with this kind of inconsistency a number of methodological changes to the original rough sets theory is necessary. The main change is the substitution of the indiscernibility relation by a dominance relation, which permits approximation of ordered sets in multicriteria sorting. To approximate preference relations in multicriteria choice and ranking problems, another change is necessary: substitution of the data table by a pairwise comparison table, where each row corresponds to a pair of objects described by binary relations on particular criteria. In all those MCDA problems, the new rough set approach ends with a set of decision rules playing the role of a comprehensive preference model. It is more general than the classical functional or relational model and it is more understandable for the users because of its natural syntax. In order to workout a recommendation in one of the MCDA problems, we propose exploitation procedures of the set of decision rules. Finally, some other recently obtained results are given: rough approximations by means of similarity relations, rough set handling of missing data, comparison of the rough set model with Sugeno and Choquet integrals, and results on equivalence of a decision rule preference model and a conjoint measurement model which is neither additive nor transitive.

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Topics: Dominance-based rough set approach (79%), Rough set (62%), Weighted product model (61%) ...read more

1,436 Citations


Journal ArticleDOI: 10.1016/J.ESWA.2007.09.031
Zhi Xiao1, Shi-Jie Ye1, Bo Zhong1, Cai-Xin Sun1Institutions (1)
Abstract: Precise Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors to develop the accuracy of predictions. Through attribute reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased. Using load time series from a practical power system, we tested the performance of RSBP by comparing its predictions with that of BP network.

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Topics: Rough set (50%)

197 Citations


Open accessJournal Article
Abstract: The formal concept analysis gives a mathematical definition of a formal concept. However, in many real-life applications, the problem under investigation cannot be described by formal concepts. Such concepts are called the non-definable concepts (Saquer and Deogun, 2000a). The process of finding formal concepts that best describe non-definable concepts is called the concept approximation. In this paper, we present two different approaches to the concept approximation. The first approach is based on rough set theory while the other is based on a similarity measure. We present algorithms for the two approaches.

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96 Citations

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