My Account | Careers | Downloads | Blog

PROJECT TITLE :

Machine-Learning Aided Optimal Customer Decisions for an Interactive Smart Grid

ABSTRACT:

During this paper, a hierarchical good grid design is presented. The concept of good house is extended in 2 aspects: 1) from traditional households with sensible devices, like advanced metering infrastructure, to intelligent entities with instantaneous and distributive decision-creating capabilities; and a pair of) from individual households to general customer units of presumably large scales. We then develop a hidden mode Markov decision method (HM-MDP) model for a customer real-time call-making problem. This real-time call-making framework will effectively be integrated with demand response schemes, which are prediction primarily based and so inevitably lead to real-time power-load mismatches. With the Baum–Welch algorithm adopted to be told the nonstationary dynamics of the setting, we have a tendency to propose a worth iteration (VI)-based mostly actual resolution algorithm for the HM-MDP downside. In contrast to typical VI, the concept of parsimonious sets is employed to enable a finite illustration of the optimal value perform. Instead of iterating the price function in every time step, we iterate the representational parsimonious sets by using the incremental pruning algorithm. Although this actual algorithm ends up in optimal policies giving maximum rewards for the smart homes, its complexity suffers from the curse of dimensionality. To obtain a low-complexity real-time algorithm that enables adaptively incorporating new observations because the environment changes, we tend to resort to Q-learning-based approximate dynamic programming. Q-learning offers a lot of flexibility in observe as a result of it does not require specific beginning and ending points of the scheduling amount. Performance analysis of each precise and approximate algorithms, as compared with the other possible different call-making ways, is presented in simulation results.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings ABSTRACT: Because of the complexity, costs, and long wait times associated with sleep apnea-hypopnea
PROJECT TITLE : On the Scalability of Machine-Learning Algorithms for Breast Cancer Prediction in Big Data Context ABSTRACT: Data has grown at an exponential rate as a result of recent developments in information technology, ushering
PROJECT TITLE :Optimal, Efficient Sequential Control of a Soft-Bodied, Peristaltic Sorting TableABSTRACT:A peristaltic, soft-bodied xy-sorting table manipulates objects by producing moving wave shapes on its surface. The waves
PROJECT TITLE :Powered Two-Wheeler Riding Pattern Recognition Using a Machine-Learning FrameworkABSTRACT:During this paper, a machine-learning framework is employed for riding pattern recognition. The problem is formulated as
PROJECT TITLE : Security Analysis of Handover Key Management in 4G LTESAE Networks - 2014 ABSTRACT: The goal of 3GPP Long Term Evolution/System Architecture Evolution (LTE/SAE) is to move mobile cellular wireless technology

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry