26,661 research outputs found
Characterisation of large changes in wind power for the day-ahead market using a fuzzy logic approach
Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution Numerical Weather Prediction (NWP) models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power out- put that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using Genetic Programming (GP) and Quantile Regression Forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented using colour maps, which provide a useful way to interpret the characteristics of the ramp events
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Detection of non-technical losses (NTL) which include electricity theft,
faulty meters or billing errors has attracted increasing attention from
researchers in electrical engineering and computer science. NTLs cause
significant harm to the economy, as in some countries they may range up to 40%
of the total electricity distributed. The predominant research direction is
employing artificial intelligence to predict whether a customer causes NTL.
This paper first provides an overview of how NTLs are defined and their impact
on economies, which include loss of revenue and profit of electricity providers
and decrease of the stability and reliability of electrical power grids. It
then surveys the state-of-the-art research efforts in a up-to-date and
comprehensive review of algorithms, features and data sets used. It finally
identifies the key scientific and engineering challenges in NTL detection and
suggests how they could be addressed in the future
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
Clustering outdoor soundscapes using fuzzy ants
A classification algorithm for environmental sound recordings or "soundscapes" is outlined. An ant clustering approach is proposed, in which the behavior of the ants is governed by fuzzy rules. These rules are optimized by a genetic algorithm specially designed in order to achieve the optimal set of homogeneous clusters. Soundscape similarity is expressed as fuzzy resemblance of the shape of the sound pressure level histogram, the frequency spectrum and the spectrum of temporal fluctuations. These represent the loudness, the spectral and the temporal content of the soundscapes. Compared to traditional clustering methods, the advantages of this approach are that no a priori information is needed, such as the desired number of clusters, and that a flexible set of soundscape measures can be used. The clustering algorithm was applied to a set of 1116 acoustic measurements in 16 urban parks of Stockholm. The resulting clusters were validated against visitor's perceptual measurements of soundscape quality
Intelligent Financial Fraud Detection Practices: An Investigation
Financial fraud is an issue with far reaching consequences in the finance
industry, government, corporate sectors, and for ordinary consumers. Increasing
dependence on new technologies such as cloud and mobile computing in recent
years has compounded the problem. Traditional methods of detection involve
extensive use of auditing, where a trained individual manually observes reports
or transactions in an attempt to discover fraudulent behaviour. This method is
not only time consuming, expensive and inaccurate, but in the age of big data
it is also impractical. Not surprisingly, financial institutions have turned to
automated processes using statistical and computational methods. This paper
presents a comprehensive investigation on financial fraud detection practices
using such data mining methods, with a particular focus on computational
intelligence-based techniques. Classification of the practices based on key
aspects such as detection algorithm used, fraud type investigated, and success
rate have been covered. Issues and challenges associated with the current
practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and
Privacy in Communication Networks (SecureComm 2014
Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction
British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225
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