A Balancing Demand Response Clustering Approach of Domestic Electricity for Day-Ahead Markets

This paper introduces a new clustering approach for multi-customer intelligent demand response for customers living in the same or closer smart grid locations using real electricity consumption data from smart meters. Most of the demand side management or customer tariffs focused on a single customer to optimize their usage discarding the others connected to the same grid. The proposed balancing clustering focus on the customers connected to the same or closest grid to optimize the smooth operating of the energy producers. This approach offers a triple win-win-win model for peak and low consumption customers as well as the balancing for the producer/ distributor utility companies for planning the day ahead markets. This paper uses the most widely used clustering method of k-means for finding similar customers on the opposing side peak, low consumption profiles and combines the most distinguished customers forming more uniform consumption for day-ahead market. This customer balancing and grouping them provides a better way toaggregate residential load data for power buy and sell for all sides and results in better load scheduling

Credit Risk Evaluation as a Service (CREaaS) based on ANN and Machine Learning

Credit risk evaluation is the major concern of the banks and financial institutions since there is a huge competition between them to find the minimum risk and maximum amount of credits supplied. Comparing with the other services of the banks like credit cards, value added financial services, account management and money transfers, the majority of their capitals has been used for various types of credits. Even there is a competition among them for finding and serving the low risk customers, these institution shares limited information about the risk and risk related information for the common usage. The purpose of this paper is to explain the service oriented architecture and the decision model for those banks which shares the information about their customers and makes potential customer analysis. Credit Risk Evaluation as a Service system, provides a novel service based information retrieval system submitted by the banks and institutions. The system itself has a sustainable, supervised learning with continuous improvement with the new data submitted. As a main concern of conflict of interest between the institutions trade and privacy information secured for internal usage and full encrypted data gathering and as well as storing architecture with encryption. Proposed system architecture and model is designed mainly for the commercial credits for SME’s due to the complexity and variety of other credits

Workforce Optimization for Bank Operation Centers: A Machine Learning Approach

Online Banking Systems evolved and improved in recent years with the use of mobile and online technologies, performing money transfer transactions on these channels can be done without delay and human interaction, however commercial customers still tend to transfer money on bank branches due to several concerns. Bank Operation Centers serve to reduce the operational workload of branches. Centralized management also offers personalized service by appointed expert employees in these centers. Inherently, workload volume of money transfer transactions changes dramatically in hours. Therefore, work-force should be planned instantly or early to save labor force and increase operational efficiency. This paper introduces a hybrid multi stage approach for workforce planning in bank operation centers by the application of supervised and unsu-pervised learning algorithms. Expected workload would be predicted as supervised learning whereas employees are clus-tered into different skill groups as unsupervised learning to match transactions and proper employees. Finally, workforce optimization is analyzed for proposed approach on production data