Damminda Alahakoon

Dynamic self-organizing maps with controlled growth for knowledge discovery

The growing self-organizing map (GSOM) has been presented as an extended version of the self-organizing map (SOM), which has significant advantages for knowledge discovery applications. In this paper, the GSOM algorithm is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the GSOM, is investigated. The spread factor is independent of the dimensionality of the data and as such can be used as a controlling measure for generating maps with different dimensionality, which can then be compared and analyzed with better accuracy. The spread factor is also presented as a method of achieving hierarchical clustering of a data set with the GSOM. Such hierarchical clustering allows the data analyst to identify significant and interesting clusters at a higher level of the hierarchy, and as such continue with finer clustering of only the interesting clusters. Therefore, only a small map is created in the beginning with a low spread factor, which can be generated for even a very large data set. Further analysis is conducted on selected sections of the data and as such of smaller volume. Therefore, this method facilitates the analysis of even very large data sets.

A Data Mining Framework for Electricity Consumption Analysis From Meter Data

This paper presents a novel data mining framework for the exploration and extraction of actionable knowledge from data generated by electricity meters. Although a rich source of information for energy consumption analysis, electricity meters produce a voluminous, fast-paced, transient stream of data that conventional approaches are unable to address entirely. In order to overcome these issues, it is important for a data mining framework to incorporate functionality for interim summarization and incremental analysis using intelligent techniques. The proposed Incremental Summarization and Pattern Characterization (ISPC) framework demonstrates this capability. Stream data is structured in a data warehouse based on key dimensions enabling rapid interim summarization. Independently, the IPCL algorithm incrementally characterizes patterns in stream data and correlates these across time. Eventually, characterized patterns are consolidated with interim summarization to facilitate an overall analysis and prediction of energy consumption trends. Results of experiments conducted using the actual data from electricity meters confirm applicability of the ISPC framework.

Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey

Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social, and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics, which deals with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interests. Furthermore, the paper highlights challenges as well as opportunities arising due to the advent of big data and the increasing popularity of cloud environments.

Improving the adaptability in automated vessel scheduling in container ports using intelligent software agents

Abstract Faster turnaround time of vessels and high berth productivity are paramount factors in container terminals for assuring competitive advantage in the shipping industry. An autonomous decision-making capability in the terminal is vital in achieving the required productivity. Vessel scheduling/berthing system in a container terminal is regarded as a very complex dynamic application in today’s business world. The Artificial Intelligence (AI) community has been researching in the field of intelligent (or rational) agents for more than a decade and implementations are found in many commercial applications. The Beliefs, Desires and Intention (BDI) agent architecture is probably the most mature model for many industrial applications in today’s context. However, it is not the best agent model for complex applications that must learn and adapt their behaviours in making rational decisions. We propose a new hybrid BDI framework with an intelligent module to overcome the limitations in the generic BDI model. Learning and the adaptability of the environments are assured with the introduction of the Knowledge Acquisition Module (KAM) in the generic BDI architecture in our proposed framework. The dynamic selection of the intention structures has been improved with a trained neural network. The knowledge required to handle vagueness or uncertainty in the environment has been modelled with an Adaptive Neuro Fuzzy Inference System (ANFIS) in berths. Finally, the benefits and the usability of hybrid BDI model for a vessel berthing application is discussed with experiential results.

A self-growing cluster development approach to data mining

We describe a data analysis method using a structure adapting neural network with two additional layers. The neural network used is an extended version of a self-organising feature map which can adapt its structure to better represent the clusters in data. Once the clusters are identified, we use two additional layers on the feature map to analyse the clusters and the representation of attributes in the clusters. Simulations and initial results with two simple benchmark data sets are also described.

Advanced analytics for harnessing the power of smart meter big data

Smart meters or advanced metering infrastructure (AMI) are being deployed in many countries around the world. Smart meters are the basic building block of the smart grid and governments have invested vast amounts in smart meter deployment targeting wide economic, social and environmental benefits. The key functionality of the smart meter is the capture and transfer of data relating to the consumption (electricity, gas) and events such as power quality and meter status. Such capability has also resulted in the generation of an unprecedented data volume, speed of collection and complexity, which has resulted in the so called big data challenge. To realize the hidden value and power in such data, it is important to use the appropriate tools and technology which are currently being called advanced analytics. In this paper we define a smart metering landscape and discuss different technologies available for harnessing the smart meter captured data. Main limitations and challenges with existing techniques with big data are also highlighted and several future directions in smart metering are presented.

Cluster identification and separation in the growing self-organizing map: application in protein sequence classification

Growing self-organizing map (GSOM) has been introduced as an improvement to the self-organizing map (SOM) algorithm in clustering and knowledge discovery. Unlike the traditional SOM, GSOM has a dynamic structure which allows nodes to grow reflecting the knowledge discovered from the input data as learning progresses. The spread factor parameter (SF) in GSOM can be utilized to control the spread of the map, thus giving an analyst a flexibility to examine the clusters at different granularities. Although GSOM has been applied in various areas and has been proven effective in knowledge discovery tasks, no comprehensive study has been done on the effect of the spread factor parameter value to the cluster formation and separation. Therefore, the aim of this paper is to investigate the effect of the spread factor value towards cluster separation in the GSOM. We used simple k-means algorithm as a method to identify clusters in the GSOM. By using Davies–Bouldin index, clusters formed by different values of spread factor are obtained and the resulting clusters are analyzed. In this work, we show that clusters can be more separated when the spread factor value is increased. Hierarchical clusters can then be constructed by mapping the GSOM clusters at different spread factor values.

Incremental pattern characterization learning and forecasting for electricity consumption using smart meters

This paper presents a novel methodology for the incremental characterization and prediction of electricity consumption based on smart meter readings. A self-learning algorithm is developed to incrementally discover patterns in a data stream environment and sustain acquired knowledge for subsequent learning. It generates an evolving columnar structure composed of learning outcomes from each phase. This columnar structure characterizes electricity consumption and thus exposes significant patterns and continuity over time. The proposed technique is applied to smart meter data collected from RMIT University premises. Results show the potential for incremental pattern characterization learning in electricity consumption analysis and forecasting.

Controlling the spread of dynamic self-organising maps

The growing self-organising map (GSOM) has recently been proposed as an alternative neural network architecture based on the traditional self-organising map (SOM). The GSOM provides the user with the ability to control the spread of the map by defining a parameter called the spread factor (SF), which results in enhanced data mining and hierarchical clustering opportunities. When experimenting with the SOM, the grid size (number of rows and columns of nodes) can be changed until a suitable cluster distribution is achieved. In this paper we highlight the effect of the spread factor on the GSOM and contrast this effect with grid size change (increase and decrease) in the SOM. We also present experimental results in support of our claims regarding differences between GSOM and SOM.

Semi-supervised classification of characterized patterns for demand forecasting using smart electricity meters

Smart meters are being gradually adopted by energy providers for commercial use due to multiple benefits. The extraction of actionable knowledge from smart meter readings can lead to informed decision-making in demand forecasting and consumption analysis. This paper extends an incremental learning approach for pattern characterization in a smart meter data stream environment, with the incorporation of a semi-supervised classification feature. The incremental pattern characterization learning (IPCL) algorithm autonomously learns from a smart meter environment and accumulates patterns in a columnar structure. The introduction of semi-supervised classification improves the quality and usability of the learning outcomes. We report outcomes demonstrating the classification of incremental learning outcomes, separation of cyclic patterns from exceptions, and a capacity to interpose new dimensions from the problem domain.