A5101669622- Smart Grid Energy Management
- Energy Load and Power Forecasting
- Energy Efficiency and Management
- Electricity Theft Detection Techniques
- Imbalanced Data Classification Techniques
- Flood Risk Assessment and Management
- Heat transfer and supercritical fluids
- Smart Grid Security and Resilience
- Multidisciplinary Science and Engineering Research
- Smart Cities and Technologies
- Coal Combustion and Slurry Processing
COMSATS University Islamabad
2019-2020
Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. about electricity load price provides future trends patterns of consumption. There a loss in generation use electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead forecasting beneficial for both suppliers consumers. In this paper, Deep Learning (DL) data mining techniques forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature...
Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making process optimization strategies for complex industrial operations. In this paper, modern reliable modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), least square support vector machine (LSSVM), employed comprehensively compared as robust models generator power a 660 MWe supercritical coal combustion...
Electricity theft is one of the main causes non-technical losses and its detection important for power distribution companies to avoid revenue loss. The advancement traditional grids smart allows a two-way flow information energy that enables real-time management, billing load surveillance. This infrastructure automate electricity (ETD) by constructing new innovative data-driven solutions. Whereas, ETD approaches do not provide acceptable performance due high-dimensional imbalanced data,...