An Evidence Based Approach To Determining Residential Occupancy and its Role in Demand Response Management

AbstractThis article introduces a methodological approach for analysing time series data from multiple sensors in order to estimate home occupancy. The approach combines the Dempster-Shafer theory, which allows the fusion of ‘evidence’ from multiple sensors, with the Hidden Markov Model. The procedure addresses some of the practicalities of occupancy estimation including the blind estimation of sensor distributions during unoccupied and occupied states, and issues of occupancy inference when some sensors have missing data. The approach is applied to preliminary data from a residential family home on the North Coast of Scotland. Features derived from sensors that monitored electrical power, dew point temperature and indoor CO2 concentration were fused and the Hidden Markov Model applied to predict the occupancy profile. The approach shown is able to predict daytime occupancy, while effectively handling periods of missing sensor data, according to cross-validation with available ground truth information. Knowledge of occupancy is then fused with consumption behaviour and a simple metric developed to allow the assessment of how likely it is that a household can participate in demand response at different periods during the day. The benefits of demand response initiatives are qualitatively discussed. The approach could be used to assist in the transition towards more active energy citizens, as envisaged by the smart grid

Software quality and reliability prediction using Dempster -Shafer theory

As software systems are increasingly deployed in mission critical applications, accurate quality and reliability predictions are becoming a necessity. Most accurate prediction models require extensive testing effort, implying increased cost and slowing down the development life cycle. We developed two novel statistical models based on Dempster-Shafer theory, which provide accurate predictions from relatively small data sets of direct and indirect software reliability and quality predictors. The models are flexible enough to incorporate information generated throughout the development life-cycle to improve the prediction accuracy.;Our first contribution is an original algorithm for building Dempster-Shafer Belief Networks using prediction logic. This model has been applied to software quality prediction. We demonstrated that the prediction accuracy of Dempster-Shafer Belief Networks is higher than that achieved by logistic regression, discriminant analysis, random forests, as well as the algorithms in two machine learning software packages, See5 and WEKA. The difference in the performance of the Dempster-Shafer Belief Networks over the other methods is statistically significant.;Our second contribution is also based on a practical extension of Dempster-Shafer theory. The major limitation of the Dempsters rule and other known rules of evidence combination is the inability to handle information coming from correlated sources. Motivated by inherently high correlations between early life-cycle predictors of software reliability, we extended Murphy\u27s rule of combination to account for these correlations. When used as a part of the methodology that fuses various software reliability prediction systems, this rule provided more accurate predictions than previously reported methods. In addition, we proposed an algorithm, which defines the upper and lower bounds of the belief function of the combination results. To demonstrate its generality, we successfully applied it in the design of the Online Safety Monitor, which fuses multiple correlated time varying estimations of convergence of neural network learning in an intelligent flight control system

Decision-Making with Belief Functions: a Review

Approaches to decision-making under uncertainty in the belief function framework are reviewed. Most methods are shown to blend criteria for decision under ignorance with the maximum expected utility principle of Bayesian decision theory. A distinction is made between methods that construct a complete preference relation among acts, and those that allow incomparability of some acts due to lack of information. Methods developed in the imprecise probability framework are applicable in the Dempster-Shafer context and are also reviewed. Shafer's constructive decision theory, which substitutes the notion of goal for that of utility, is described and contrasted with other approaches. The paper ends by pointing out the need to carry out deeper investigation of fundamental issues related to decision-making with belief functions and to assess the descriptive, normative and prescriptive values of the different approaches

Innovative Two-Stage Fuzzy Classification for Unknown Intrusion Detection

Intrusion detection is the essential part of network security in combating against illegal network access or malicious cyberattacks. Due to the constantly evolving nature of cyber attacks, it has been a technical challenge for an intrusion detection system (IDS) to effectively recognize unknown attacks or known attacks with inadequate training data. Therefore in this dissertation work, an innovative two-stage classifier is developed for accurately and efficiently detecting both unknown attacks and known attacks with insufficient or inaccurate training information. The novel two-stage fuzzy classification scheme is based on advanced machine learning techniques specifically for handling the ambiguity of traffic connections and network data. In the first stage of the classification, a fuzzy C-means (FCM) algorithm is employed to softly compute and optimize clustering centers of the training datasets with some degree of fuzziness counting for feature inaccuracy and ambiguity in the training data. Subsequently, a distance-weighted k-NN (k-nearest neighbors) classifier, combined with the Dempster-Shafer Theory (DST), is introduced to assess the belief functions and pignistic probabilities of the incoming data associated with each of known classes to further address the data uncertainty issue in the cyberattack data. In the second stage of the proposed classification algorithm, a subsequent classification scheme is implemented based on the obtained pignistic probabilities and their entropy functions to determine if the input data are normal, one of the known attacks or an unknown attack. Secondly, to strengthen the robustness to attacks, we form the three-layer hierarchy ensemble classifier based on the FCM weighted k-NN DST classifier to have more precise inferences than those made by a single classifier. The proposed intrusion detection algorithm is evaluated through the application of the KDD’99 datasets and their variants containing known and unknown attacks. The experimental results show that the new two-stage fuzzy KNN-DST classifier outperforms other well-known classifiers in intrusion detection and is especially effective in detecting unknown attacks