The Evolution of OSI Network Management by Integrated the Expert Knowledge

The management of modern telecommunications networks must satisfy ever-increasing operational demands. Operation and quality service requirements imposed by the users are also an important aspect to consider. In this paper we have carried out a study for the improvement of intelligent administration techniques in telecommunications networks. This task is achieved by integrating knowledge base of expert system within the management information used to manage a network. For this purpose, an extension of OSI management framework specifications language has been added and investigated in this study. A new property named RULE has also been added, which gathers important aspects of the facts and the knowledge base of the embedded expert system. Networks can be managed easily by using this proposed integration

Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes

Datacab: a geographical‐information‐system‐based expert system for the design of cable networks

Telecommunication networks have evolved over time as a result of technological advances, and network topologies and equipment have become increasingly complex. Expert systems are being successfully applied to the management of telecommunication networks. However, applying expert systems to network design is another especially beneficial yet still not very common approach. In this paper we propose a rule‐based expert system called Datacab. Datacab was developed at Enditel Endesa in collaboration with the Electronic Technology Department of the University of Seville, for the automatic design of hybrid fibre coax (HFC) cable networks. Using data from a geographical information system as input, it automatically generates viable HFC network designs

A Precise Electrical Disturbance Generator for Neural Network Training with Real Level Output

Power Quality is defined as the study of the quality of electric power lines. The detection and classification of the different disturbances which cause power quality problems is a difficult task which requires a high level of engineering expertise. Thus, neural networks are usually a good choice for the detection and classification of these disturbances. This paper describes a powerful tool, developed by the Institute for Natural Resources and Agrobiology at the Scientific Research Council (CSIC) and the Electronic Technology Department at the University of Seville, which generates electrical patterns of disturbances for the training of neural networks for PQ tasks. This system has been expanded to other applications (as comparative test between PQ meters, or test of effects of power-line disturbances on equipment) through the addition of a specifically developed high fidelity power amplifier, which allows the generation of disturbed signals at real levels.Ministerio de Ciencia y Tecnología DPI2006-15467-C02-0

An Expert System to Improve the Energy Efficiency of the Reaction Zone of a Petrochemical Plant

Energy is the most important cost factor in the petrochemical industry. Thus, energy efficiency improvement is an important way to reduce these costs and to increase predictable earnings, especially in times of high energy price volatility. This work describes the development of an expert system for the improvement of this efficiency of the reaction zone of a petrochemical plant. This system has been developed after a data mining process of the variables registered in the plant. Besides, a kernel of neural networks has been embedded in the expert system. A graphical environment integrating the proposed system was developed in order to test the system. With the application of the expert system, the energy saving on the applied zone would have been about 20%.Junta de Andalucía TIC-570

MIDAS: Detection of Non-technical Losses in Electrical Consumption Using Neural Networks and Statistical Techniques

Datamining has become increasingly common in both the public and private sectors. A non-technical loss is defined as any consumed energy or service which is not billed because of measurement equipment failure or ill-intentioned and fraudulent manipulation of said equipment. The detection of non-technical losses (which includes fraud detection) is a field where datamining has been applied successfully in recent times. However, the research in electrical companies is still limited, making it quite a new research topic. This paper describes a prototype for the detection of non-technical losses by means of two datamining techniques: neural networks and statistical studies. The methodologies developed were applied to two customer sets in Seville (Spain): a little town in the south (pop: 47,000) and hostelry sector. The results obtained were promising since new non-technical losses (verified by means of in-situ inspections) were detected through both methodologies with a high success rate

An Approach to Detection of Tampering in Water Meters

Meter tampering is defined as a fraudulent manipulation which implies a service that is not billed by a utility company. It is a lack of consumption control for the utility company and a main problem because they represent an important loss of income. We have developed a methodology consists of a set of three algorithms for the detection of meter tampering in the Emasesa Company (a water distribution company in Seville and one of the most important of the country). The algorithms were generated and programmed after a data mining process from the database of the company and they detect three type of consumption patterns: Progressive drops, sudden drops and abnormally low consumption. The methodology has been tested with in situ inspections of the customers of a village of the province of Seville. Once carried out the inspections by the utility, the inspectors confirmed a good success rate taking into account that the detection of this type of fraud is very difficult because it is a noninvasive technique. Besides, this type of detections is a topic that, if we take a look at the state of the art, there are few references or works.Ministerio de Ciencia y Tecnología TEC2013-40767-

Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach

The rapidly growing world energy use already has concerns over the exhaustion of energy resources andheavy environmental impacts. As a result of these concerns, a trend of green and smart cities has beenincreasing. To respond to this increasing trend of smart cities with buildings every time more complex,in this paper we have proposed a new method to solve energy inefficiencies detection problem in smartbuildings. This solution is based on a rule-based system developed through data mining techniques andapplying the knowledge of energy efficiency experts. A set of useful energy efficiency indicators is alsoproposed to detect anomalies. The data mining system is developed through the knowledge extracted bya full set of building sensors. So, the results of this process provide a set of rules that are used as a partof a decision support system for the optimisation of energy consumption and the detection of anomaliesin smart buildings.Comisión Europea FP7-28522

Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection

Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers

Electricity clustering framework for automatic classification of customer loads

Clustering in energy markets is a top topic with high significance on expert and intelligent systems. The main impact of is paper is the proposal of a new clustering framework for the automatic classification of electricity customers’ loads. An automatic selection of the clustering classification algorithm is also highlighted. Finally, new customers can be assigned to a predefined set of clusters in the classificationphase. The computation time of the proposed framework is less than that of previous classification tech- niques, which enables the processing of a complete electric company sample in a matter of minutes on a personal computer. The high accuracy of the predicted classification results verifies the performance of the clustering technique. This classification phase is of significant assistance in interpreting the results, and the simplicity of the clustering phase is sufficient to demonstrate the quality of the complete mining framework.Ministerio de Economía y Competitividad TEC2013-40767-RMinisterio de Economía y Competitividad IDI- 2015004