What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
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.
His most cited work include:
- Demand Side Management in Smart Grid Using Heuristic Optimization (765 citations)
- An Introduction to Multi-Agent Systems (376 citations)
- Neural-network-based signature recognition for harmonic source identification (342 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Mathematical optimization (22.85%)
- Artificial intelligence (19.90%)
- Artificial neural network (16.95%)
What were the highlights of his more recent work (between 2015-2021)?
- Mathematical optimization (22.85%)
- Smart grid (10.57%)
- Distributed generation (9.58%)
In recent papers he was focusing on the following fields of study:
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.
Between 2015 and 2021, his most popular works were:
- A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (288 citations)
- Smart Charging Strategies for Optimal Integration of Plug-In Electric Vehicles Within Existing Distribution System Infrastructure (99 citations)
- Multiagent-Based Transactive Energy Framework for Distribution Systems With Smart Microgrids (95 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
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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