IEEE Computational Intelligence Society Research Articles

Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge

Effective operation of a microgrid depends critically on accurate forecasting of its components. Recently, internet forecasting competitions have been used to determine the best methods for energy forecasting, with some competitions having a special focus on microgrids and COVID-19 energy-use forecasting. This paper describes forecasting for the IEEE Computational Intelligence Society 3rd Technical Challenge, which required predicting solar and building loads of a microgrid system at Monash University for the month of November 2020. The forecast achieved the lowest error rate in the competition. We review the literature on recent energy forecasting competitions and metrics and explain how the solution drew from top-ranked solutions in previous energy forecasting competitions such as the Global Energy Forecasting Competition series. The techniques can be reapplied in other forecasting endeavours, while approaches to some of the time-series forecasting are more ad hoc and specific to the competition. Novel thresholding approaches were used to improve the quality of the input data. As the training and evaluation phase of the challenge occurred during COVID-19 lockdown and reopening, the building demand was subject to pandemic-related effects. Finally, we assess other data sources which would have improved the model forecast skill such as data from different numerical weather prediction (NWP) models, solar observations, and high-resolution price and demand data in the vicinity of the campus.

AI by Any Other Name [President’s Message

Hi all, To borrow from The Bard, what’s in a name? The world is awash with the hype, the reality, the potential, and the concerns of Artificial Intelligence. But what constitutes AI? That depends on who, and when, you ask. When I was a kid (academically speaking), tasked with teaching a course on AI, I “knew” what it was: systems with propositions consisting of discrete symbols, manipulated through first order predicate calculus, sometimes with a helping of probability on the side. There were languages and hardware developed to assist in that enterprise. So, when the IEEE Neural Networks Council/Society changed its name to the IEEE Computational Intelligence Society, it made perfect sense to me that CI was different from AI since the underlying models were rooted in numeric and functional representations, producing a nice crisp partition of techniques.

Ghostbusters and CIS [President’s Message

Hey all, Well, it looks like I’m the President of the IEEE Computational Intelligence Society for 2022-2023. Talk about your “hard act to follow!” I have great admiration and affection for Bernadette Bouchon-Meunier who led us through the unprecedented time of the COVID-19 pandemic with such grace and acumen. I have been fortunate to have worked with her over the years in several capacities, have had an inside view of CIS as her President-elect in 2021, but most of all to be able to call her “mon amie”. She’ll continue to provide leadership as past president in 2022 to my great relief. Bernadette followed a long line of dedicated and innovative Presidents; CIS has been fortunate for their leadership. Now it’s my turn “in the barrel”. The good news for me (and for you!) is that we have a large cast of extremely talented folks who carry the brunt of the organizational load. The Vice Presidents are energic and committed. Besides Finance, Publications, Conferences, Technical Activities, Education, and Membership, 2022 will see a VP in new focus area, Industry and Government Activities. The leadership of the CIS standing committees, subcommittees, ad hoc committees, EiCs and AEs, and conference chairs do so much to ensure that all goes well within CIS. Also, we are blessed with 3 outstanding IEEE staff members who keep the wheels of CIS rolling smoothly.

After Two Years [President’s Message

This is my last message to you as President of the IEEE Computational Intelligence Society, after two years of living with the pandemic. What happened during these two years? Progress has been made on our priorities.

Darrell Whitley Awarded as the 2022 Evolutionary Computation Pioneer

Prof. Darrell Whitley has received the 2022 IEEE Pioneer Award on Evolutionary Computation from the IEEE Computational Intelligence Society "For early contributions to the practical implementation and theoretical understanding of evolutionary algorithms."

Ethics, Diversity and Consciousness in AI [President's Message

Ethics in Artificial Intelligence (AI) is discussed everywhere, in governmental circles as well as scientific forums. It is very often associated with the two widely used concepts of diversity and eXplainable Artificial Intelligence (XAI). The latter was promoted by DARPA, and it is expected to propose methods preserving the rights of users to understand how the AI systems work and why decisions are made. Computational Intelligence methods have much to contribute to this effort to ensure that AI systems are ethical. In July 2020, the European Union has published an Assessment List for Trustworthy Artificial Intelligence (ALTAI) covering a wide range of ethical issues. The Computational Intelligence society (CIS) is at the heart of all these aspects of ethics in AI. They represent fascinating challenges for researchers and they can highlight the power of all Computational Intelligence methods: neural networks, learning methods, fuzzy systems and evolutionary computation. Efforts are already done through the IEEE CIS Task Force on Ethical and Social Implications of Computational Intelligence. Several other CIS Technical Committee task forces and special issues of CIS publications are also focused on XAI. In particular, a special issue of the IEEE Computational Intelligence Magazine dedicated to Explainable and Trustworthy Artificial Intelligence will be published in 2022. The IEEE CIS Cognitive and Developmental Systems Technical Committee and the IEEE Transactions on Cognitive and Developmental Systems also address some of these concerns on a cognitive basis. Moreover, the IEEE CIS is the oversight committee for the IEEE Consortium On The Landscape of AI Safety (CLAIS) and, on a more specific level, it participates in the IEEE Brain Community. I am convinced that the IEEE CIS needs to pay even more attention to these crucial issues.

Enhanced Velocity Differential Evolutionary Particle Swarm Optimization for Optimal Scheduling of a Distributed Energy Resources With Uncertain Scenarios

In the MicroGrid environment, the high penetration of uncertain energy sources such as solar Photovoltaics (PVs), Energy Storage Systems (ESSs), Demand Response (DR) programs, Vehicles to Grid (V2G or G2V) and Electricity Markets make the Energy Resource Management (ERM) problem highly complex. All such complexities should be addressed while maximizing income and minimizing the total operating costs of aggregators that accumulate all types of available energy resources from the MicroGrid system. Due to the presence of mixed-integer, discrete variables and non-linear network constraints, it is sometimes very difficult to solve such problem using traditional optimization methods. This paper proposes a new metaheuristic optimization technique entitled the “Enhanced Velocity Differential Evolutionary Particle Swarm Optimization” (EVDEPSO) algorithm to tackle the ERM problem. Its key feature is the updation of the Velocity by the terms named as Enhanced Velocity, Cooperation and Stochastic Uni-Random Distribution and position by the term Deceleration Factor. The performance of the proposed method is measured by a case study comprises of 100 scenarios of a 25-bus MicroGrid with high penetration of aforementioned energy sources. IEEE Computational Intelligence Society organized the competition on the above mentioned problem, in which EVDEPSO secured a second rank. The results of EVDEPSO are compared with the competition participated optimization algorithms. It also compared with well-known optimization algorithms such as Variable Neighborhood Search and Differential Evolutionary Particle Swarm Optimization. The comparison results show that the performance of EVDEPSO is superior in terms of the Ranking Index (R.I) and Average Ranking Index (A.R.I) as compared to the aforementioned algorithms. For effective comparative analysis of algorithms, standard statistical test named as One-Way ANOVA and Tukey Test is used. The results of this test also prove the effectiveness of EVDEPSO algorithm as compared to all tested algorithms.

JFML: A Java Library to Design Fuzzy Logic Systems According to the IEEE Std 1855-2016

Fuzzy logic systems are useful for solving problems in many application fields. However, these systems are usually stored in specific formats and researchers need to rewrite them to use in new problems. Recently, the IEEE Computational Intelligence Society has sponsored the publication of the IEEE Standard 1855-2016 to provide a unified and well-defined representation of fuzzy systems for problems of classification, regression, and control. The main aim of this standard is to facilitate the exchange of fuzzy systems across different programming systems in order to avoid the need to rewrite available pieces of code or to develop new software tools to replicate functionalities that are already provided by other software. In order to make the standard operative and useful for the research community, this paper presents JFML, an open source Java library that offers a complete implementation of the new IEEE standard and capability to import/export fuzzy systems in accordance with other standards and software. Moreover, the new library has associated a Website with complementary material, documentation, and examples in order to facilitate its use. In this paper, we present three case studies that illustrate the potential of JFML and the advantages of exchanging fuzzy systems among available software.

Ethics and the Social Impact of Computational Intelligence [President's Message

At the time of writing this column, I have just finished a tour in Chile & Argentina with the Executive Committee (EXCOM) of the IEEE Computational Intelligence Society (CIS). As part of our geographical outreach program we organized CIS workshops at La Serena University, in northern Chile; the University of Chile, in Santiago; and the University of Buenos Aires, in Argentina. In Chile we had the opportunity to visit the astronomy observatories located near La Serena. Dr. Chris Smith, an astronomer and the head of mission, AURA Observatory in Chile, was our knowledgeable host. The actual telescope sites are located in an area of more than 90,000 acres that includes two operational mountaintops, Cerro Tololo at 2,200 m, and Cerro Pachon at 2,750 m. Tololo is a very famous place, because the discovery of the accelerated expansion of the Universe was made in these facilities. Pachon is the place where the new generation telescope named LSST is being built.

IEEE 1849: The XES Standard: The Second IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs

IEEE 1849: The XES Standard: The Second IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs

Editorial IEEE Transactions on Emerging Topics in Computational Intelligence

Presents an editorial on the inaugural issue of this publication. This transaction is a recent publication launched by the IEEE Computational Intelligence Society (CIS), targeting a worldwide network of scientists, engineers, practitioners, and researchers who are interested in pushing the boundaries of machine intelligence capabilities forward in the quest to better tackle the complex real-world challenges of today.

IEEE 1855™: The First IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs

IEEE 1855™: The First IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs

Guest Editorial

Guest Editorial

Call for nominations / Applications for the position of the Editor-in-Chief of IEEE Transactions on Emerging Topics in Computational Intelligence

In January 2017, IEEE Computational Intelligence Society is going to launch a new Transactions named IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI). The official scope of TETCI is reproduced below: The IEEE Transactions on Emerging Topics in Computational Intelligence publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys. The Transactions on Emerging Topics in Computational Intelligence is financially sponsored by the IEEE Computational Intelligence Society and it is technically cosponsored by the Computer Society. We are looking for a suitable candidate to serve as the Editor-in-Chief of TETCI from January 1, 2017. To facilitate this, the IEEE CIS Executive Committee has formed an Ad hoc Search Committee to seek a suitable candidate to serve as the EIC of TETCI. The Search Committee solicits nominations /applications for this. Nominees/applicants should be dedicated volunteers with outstanding research profiles covering different facets of computational intelligence and extensive editorial experience. The nomination/application package should include complete CV along with a separate description (max 300 words/item) on each of the following items: Vision Statements; Editorial Experience; Summary of publishing experience in IEEE-CIS journals/magazine; IEEE CIS Volunteer Experience; Institutional Support; Networking with Community; and Why does the candidate consider himself/herself fit for this position? The nomination/application package should be emailed as a single PDF to N. R. Pal at nrpal59@gmail.com by April 15, 2016.

(Computational) Intelligence: What's in a Name?

This article is about the terms intelligence, artificial intelligence (AI), and computational intelligence (CI). Topics addressed here include 1) the historical evolution of the terms AI and CI; 2) the seductive semantics of terms such as machine learning, which owe a heavy debt to our intuitive ideas about intelligence; 3) the evolution of the IEEE Computational Intelligence Society; and 4) the role that buzzwords play in the lives of all researchers. I estimate that this article is roughly 40% facts, 10% anecdotes, 15% speculation, and 30% opinions. other 5%? It's reserved for you to fill in the blank-one option would be bull. [Parts of this article are excerpted from: J.C. Bezdek., (2015). The History, Philosophy and Development of Computat ional Intel l igence (How a simple tune became a Monster hit), in Encyclopedia of Life Support Systems (EOLSS), vol. 3, Computational Intelligence, Hisao Ishibuchi, Ed., pp. 1-22. Avai lable: ieee-cis.sightworks.net/documents/History/Bezdekeolss-CI-history.pdf.]

Guest editor’s introduction: special issue on quality issues, measures of interestingness and evaluation of data mining models

There are many data mining algorithms and methodologies for various fields and various problematic. Each data mining researcher/practitioner should describe the intrinsic quality of the discovered models and patterns. In addition he/she is faced with assessing the performance of his/her own solution(s) in order to make –fair– comparisons with state of the art approaches. Which methodology, which benchmarks, which measures of performance, which tools, which measures of interest, which scenarios, etc., should be used, and why? Every one should answer these questions, and assessing the quality and the performance of a data mining process is a critical issue. Assessing the quality and the performance is a critical issue for classical situations and even more in the Big Data era where we can not manage this issue with classical and current approaches. Clearly, large scale data, complex data and streaming data bring new challenges (e.g. large scale inference, fake correlations, necessity of perpetual validation). In this setting, the Quality Issues, Measures of Interestingness and Evaluation of data mining models Workshop series provide an open forum for intensive discussions and the exchange of new ideas and best practices on quality issues among data mining researchers. QIMIE focuses on the theory, the techniques, the links between the discovery stage and the quality assessment stage, and the practices that can ensure that the discovered knowledge of a data mining process is of quality. It is organized with the IEEE Task Force on Evaluation and Quality issues (Data Mining and Big Data Analytics Technical Committee, IEEE Computational Intelligence Society).

On the History of Fuzzy Clustering: An Interview with Jim Bezdek and Enrique Ruspini [History

Enrique H?ctor Ruspini (Figure1) received his degree in mathematics from the University of Buenos Aires, Argentina, in 1965 and his doctoral degree in system science from the University of California at Los Angeles (UCLA) in 1977 [1]. He is a Distinguished Lecturer of the IEEE Computational Intelligence Society (CIS). Dr. Ruspini has held positions at the University of Buenos Aires, the University of Southern California, UCLA's Brain Research Institute, Hewlett-Packard Laboratories, the SRI International Artificial Intelligence (AI) Center, and the European Center for Soft Computing.

Computational Intelligence in Big Data [Guest Editorial

To react to the drastic developments in Big Data, the IEEE Computational Intelligence Society (IEEE CIS) has taken several measures, including setting up the First IEEE Symposium on Computational Intelligence in Big Data, which is going to be held within the SSCI 2014 in December 2014 in Florida, USA, and revising the name of the Technical Committee on Data Mining to expand its scope to cover Big Data Analytics. This special issue of the IEEE Computational Intelligence Magazine is one in a series of reactions of the IEEE CIS to the boom in Big Data Analytics research that aims to publish selected research articles presenting most recent advances and discussing challenges and opportunities in the application of CI techniques to Big Data. After a peer-review procedure, two papers have been selected from 12 manuscripts submitted to the Special Issue to be present in this Special Issue, representing the highest quality work on CI techniques in understanding Big Data challenges. Both papers are briefly summarized. Both papers are of review nature discussing the opportunities and promises of CI techniques along with challenges. They have raised more questions than answers, which is actually the aim of this Special Issue to trigger a wider range of interests in the CI community.

IEEE Fellows—Class of 2014 [Society Briefs

Lists the IEEE Computational Intelligence Society members who were elevated to the status of Fellow in 2014.

A tete-a-tete with Professor Xin Yao, President of the IEEE Computational Intelligence Society (CIS), 2014-2015 [Career Profile

Prof. Yao, a Professor of Computer Science at the University of Birmingham, UK, whose works have won numerous awards, has served in various executive positions in IEEE CIS and other organizations for many years. Growing up in Fuzhou, Fujian Province in Southeast China, Prof. Yao moved to Hefei in 1978 to attend university then to Beijing in 1982 for his Masters before returning to Hefei in 1985 to pursue his PhD. In 1990, a few months after his PhD thesis was submitted,Prof. Yao accepted a postdoctoral fellowship in Australia. In 1999, Prof. Yao, together with his wife and their son moved to UK where he is currently based and especially enjoys teaching "Introduction to Evolutionary Computation" and "Introduction to Natural Computation".