Imran Khan

TL;DR: A novel hybrid short-term electric load forecasting model is proposed, an integrated framework of data pre-processing and feature selection module, training and forecasting module, and an optimization module that is validated by comparing it with four recent forecasting models like Bi-level, mutual information-based artificial neural network (MI-ANN), ANN-based accurate and fast converging (AFC- ANN), and long short- term memory (LSTM) in terms of accuracy and convergence rate.

TL;DR: A novel framework is proposed for efficient energy management of residential buildings to reduce the electricity bill, alleviate peak-to-average ratio (PAR), and acquire the desired trade-off between the electricity bills and user-discomfort in the smart grid.

TL;DR: This research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR).

TL;DR: A hybrid model is developed by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information.

TL;DR: In this paper, the authors proposed a load scheduling and energy storage system management controller (LSEMC) based on heuristic algorithms i.e., genetic algorithm (GA), wind driven optimization (WDO), binary particle swarm optimization (BPSO), bacterial foraging optimization(BFO) and their suggested hybrid of GA, WDO and PSO (HGPDO) algorithm.