Demand-side management technology is a key element of the proposed smart grid, which will help utilities make more efficient use of their generation assets by reducing consumers’ energy demand during peak load periods. However, although some modern appliances can respond to price signals from the utility companies, there is a vast stock of older appliances that cannot. For such appliances, utilities must infer what appliances are operating in a home, given only the power signals on the main feeder to the home (i.e., the home’s power consumption must be disaggregated into individual appliances). We report on an in-depth investigation of multi-label classification algorithms for disaggregating appliances in a power signal. A systematic review of this research topic shows that this class of algorithms has received little attention in the literature, even though it is arguably a more natural fit to the disaggregation problem than the traditional single-label classifiers used to date. We examine a multi-label meta-classification framework (RA ${k}$ EL), and a bespoke multi-label classification algorithm (ML ${k}$ NN), employing both time-domain and wavelet-domain feature sets. We test these classifiers on two real houses from the Reference Energy Disaggregation Dataset. We found that the multilabel algorithms are effective and competitive with published results on the datasets.

Toward Non-Intrusive Load Monitoring via Multi-Label Classification

Wireless Sensor Network (WSN) is increasingly popular in the field of micro-environmental monitoring due to its promising capability. However, most systems using WSN for environmental monitoring reported in the literature are developed for specific applications without functions for exploiting user's data processing methods. In this paper, a new system is designed in detail to perform micro-environmental monitoring taking the advantages of the WSN. The application-oriented hardware working style is designed, and the system platform for data acquisition, validation, processing and visualization is systematically presented. Several strategies are proposed to guarantee the system capability in terms of extracting useful information, visualizing events to their authentic time are also described. Moreover, a web-based surveillance subsystem is presented for remote control and monitoring. In addition, the system is extensible for engineers to carry their own data analysis algorithms. Experimental results are to show the path reliability and real-time characteristics, and to display the feasibility and applicability of the developed system into practical deployment.

https://isiarticles.com/bundles/Article/pre/pdf/21633.pdf

Development of an integrated wireless sensor network micro-environmental monitoring system

Since mining frequent patterns from transactional databases involves an exponential mining space and generates a huge number of patterns, efficient discovery of user-interest-based frequent pattern set becomes the first priority for a mining algorithm. In many real-world scenarios it is often sufficient to mine a small interesting representative subset of frequent patterns. Temporal periodicity of pattern appearance can be regarded as an important criterion for measuring the interestingness of frequent patterns in several applications. A frequent pattern can be said periodic-frequent if it appears at a regular interval given by the user in the database. In this paper, we introduce a novel concept of mining periodic-frequent patterns from transactional databases. We use an efficient tree-based data structure, called Periodic-frequent pattern tree (PF-tree in short), that captures the database contents in a highly compact manner and enables a pattern growth mining technique to generate the complete set of periodic-frequent patterns in a database for user-given periodicity and support thresholds. The performance study shows that mining periodic-frequent patterns with PF-tree is time and memory efficient and highly scalable as well.

Discovering Periodic-Frequent Patterns in Transactional Databases

Sliding window-based frequent pattern mining over data streams

This essay gives a brief introduction of the Wireless Sensor Network, including its features, application, system structure, the analysis of energy dissipation behavior, the key technologies of the Wireless Sensor Network, and node location methods. What is more, it also analyses existing problems and development orientation in the future.

A survey of Wireless Sensor Networks

In order to mining frequent itemsets on data stream efficiently, a new approach was proposed in this paper. The memory efficient and accurate one-pass algorithm divides all the frequent itemsets into frequent equivalence classes and prune all the redundant itemsets except for those represent the GLB(Greatest Lower Bound) and LUB(Least Upper Bound) of the frequent equivalence class and the number of GLB and LUB is much less than that of frequent itemsets. In order to maintain these equivalence classes, A compact data structure, the frequent itemset enumerate tree (FIET) was proposed in the paper. The detailed experimental evaluation on synthetic and real datasets shows that the algorithm is very accurate in practice and requires significantly lower memory than Jin and Agrawal's one pass algorithm.

An efficient algorithm for frequent itemset mining on data streams

Considering the characteristics and location information of nodes in sensor networks, a modified directed transfer model of sensor networks and a new distributed data aggregation model based on "area" are proposed. On the basis of these new models, a novel mixed entropy data compression algorithm based on interval wavelet transforming is proposed for sensor network, according to the characteristics of data in sensor networks and the good performances of wavelet transforming in compression of the data stream. Theoretical analyses and simulation results show that, the above new methods can compress the data stream and reduce the energy costs of nodes in data transferring efficiently for sensor networks. So, it can prolong the lifetime of the whole networks to a greater degree when the above new methods are deployed with those traditional DC (data centric) routing

/pdf/an-algorithm-of-data-aggregation-based-on-data-compression-4iap9j37xo.pdf

An Algorithm of Data Aggregation Based on Data Compression for Sensor Networks

In this paper, a novel routing algorithm is proposed in which the nodes are clustered into different subnets by their geographical location, and a node with maximum surplus energy is selected as the core node in every subnet. The core node set routes for sensor data by using ant algorithm, which needs only local information to find the optimal path to the sink node distributedly. Analysis and simulation results demonstrate that this new algorithm is of better routing performance for sensor networks compared with previous algorithms such as AC and GITDC.

Subnets based distributed data-centric hierarchical ant routing for sensor networks

We propose a distributed wavelet-based algorithm which can transform irregularly sample data using Haar wavelet-based compression solutions in this paper We consider the characteristics and location information of nodes in sensor networks, a new distributed data aggregation mode based on "area" is used firstly On the basis of these new models, a novel wavelet-based irregularly sample data compression and data transform model DDCM is proposed for sensor networks Theoretical analyses and simulation results show that, the above new methods have the good ability of approximation, and can compress the data efficiently and can reduce the amount of data greatly So, it can prolong the lifetime of the whole network to a greater degree

Xie Zhi-jun

Papers

An efficient algorithm for frequent itemset mining on data streams

An Algorithm of Data Aggregation Based on Data Compression for Sensor Networks

Subnets based distributed data-centric hierarchical ant routing for sensor networks

Voronoi Tessellation Based Haar Wavelet Data Compression for Sensor Networks