His primary areas of study are Artificial intelligence, Artificial neural network, Mathematical optimization, Electric power system and Evolutionary algorithm. The study incorporates disciplines such as Machine learning, Data mining and Pattern recognition in addition to Artificial intelligence. His research in Artificial neural network intersects with topics in Smoothing, Demand forecasting, Cluster analysis, Probabilistic logic and Fuzzy logic.
The concepts of his Mathematical optimization study are interwoven with issues in Algorithm and Power system simulation. His study in Electric power system is interdisciplinary in nature, drawing from both Electrical load, Electrical network, Solar power, Microgrid and Robustness. His studies deal with areas such as Evolutionary computation, Optimization problem, Load shifting and Heuristics as well as Evolutionary algorithm.
Dipti Srinivasan focuses on Mathematical optimization, Artificial intelligence, Artificial neural network, Electric power system and Control theory. His research links Power system simulation with Mathematical optimization. His Artificial intelligence research incorporates themes from Machine learning and Pattern recognition.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Prediction interval, Algorithm, Data mining and Electrical load. His Electric power system study incorporates themes from Control engineering, Reliability engineering, Photovoltaic system and Renewable energy. His Control engineering study combines topics from a wide range of disciplines, such as Distributed computing, Multi-agent system and Microgrid.
Mathematical optimization, Smart grid, Distributed generation, Voltage and Photovoltaic system are his primary areas of study. His Mathematical optimization study frequently intersects with other fields, such as Benchmark. His studies in Distributed generation integrate themes in fields like Distributed computing, Relay, Overcurrent and Microgrid.
His research investigates the connection between Distributed computing and topics such as Multi-agent system that intersect with issues in Islanding. His Voltage research includes elements of Electronic engineering and Control theory. His Evolutionary algorithm research is multidisciplinary, incorporating elements of Evolutionary computation and Decomposition.
His primary areas of investigation include Mathematical optimization, Distributed generation, AC power, Voltage and Renewable energy. His Mathematical optimization research includes themes of Dispatchable generation and Fair-share scheduling. His biological study spans a wide range of topics, including Photovoltaic system, Distributed computing, Smart grid and Benchmark.
His work deals with themes such as Control engineering, Automotive engineering and Solar power, which intersect with Renewable energy. The Overcurrent study which covers Fuzzy logic that intersects with Control theory. His Power system simulation research focuses on Prediction interval and how it connects with Artificial intelligence.
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Demand Side Management in Smart Grid Using Heuristic Optimization
T. Logenthiran;D. Srinivasan;Tan Zong Shun.
IEEE Transactions on Smart Grid (2012)
Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals
Hao Quan;Dipti Srinivasan;Abbas Khosravi.
IEEE Transactions on Neural Networks (2014)
Neural-network-based signature recognition for harmonic source identification
D. Srinivasan;W.S. Ng;A.C. Liew.
IEEE Transactions on Power Delivery (2006)
Neural Networks for Real-Time Traffic Signal Control
D. Srinivasan;Min Chee Choy;R.L. Cheu.
IEEE Transactions on Intelligent Transportation Systems (2006)
A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition
Anupam Trivedi;Dipti Srinivasan;Krishnendu Sanyal;Abhiroop Ghosh.
IEEE Transactions on Evolutionary Computation (2017)
Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system
T. Logenthiran;Dipti Srinivasan;Ashwin M. Khambadkone.
Electric Power Systems Research (2011)
An Introduction to Multi-Agent Systems
P. G. Balaji;D. Srinivasan.
Multiagent System for Real-Time Operation of a Microgrid in Real-Time Digital Simulator
T. Logenthiran;D. Srinivasan;A. M. Khambadkone;Htay Nwe Aung.
IEEE Transactions on Smart Grid (2012)
Evolutionary computation in power systems
Vladimiro Miranda;Dipti Srinivasan;LM Proença.
power systems computation conference (1998)
Cooperative, hybrid agent architecture for real-time traffic signal control
Min Chee Choy;D. Srinivasan;R.L. Cheu.
systems man and cybernetics (2003)
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