No abstract

Electricity consumption in residential households' accounts for a huge share of energy use, which encourages researchers, policy makers towards energy conservation of residential household. The goal of Clustering using Unsupervised Machine Learning algorithms is to find likenesses in the data point of D-EC and cluster similar data points of D-EC together. This paper emphasis on application of machine learning techniques for pattern identification with the implementation of dimension reduction technique Self-Organizing Map algorithm trailed by unsupervised clustering algorithms such as K-means, Gaussian Mixture clustering, MiniBatchKMeans, Agglomerative Clustering, Spectral Clustering on D-EC data of electrical usage. Consumers classified into 4 clusters and labelled as per the usage pattern, also identified as a consistent and non-consistent consumer based on their electricity usage. A proposed methodology concluded using SOM followed by Kmeans algorithm which helpful for awareness and alerts to consumers based on consumption patterns participates in demand-supply decision making with timely recommendation.

In the magnitude of internet one need to devote extra time to investigate anticipated resource, especially when one need to search information from documents. For the higher range internet there is serious need to demand the essentiality to discover the reserved resources. One of the solutions for information retrieval from document repository is to attach tags to documents. Numerous online social bookmarking services permit users to attach tags with resources which are eventually meta-data, frequently stated as folksonomy. In current paper, authors implemented this model for information retrieval by utilizing these tags, after retrieving by using delicious API and synthesize tag cloud in an IndianUniversity to search and retrieve information from document repository.