An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network

Hafeez, Ghulam; Alimgeer, Khurram Saleem; Wadud, Zahid; Khan, Imran; Usman, Muhammad; Qazi, Abdul Baseer; Khan, Farrukh Aslam

doi:10.1109/access.2020.2989316

Cited by 74 publications

(28 citation statements)

References 44 publications

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“…Their main objectives were to alleviate peak formation and reduce the cost with constraints of user comfort. In [17], an innovative home EMC based on ANN and day-ahead grey wolf modified enhanced differential evolution algorithm (DA-GmEDE) is suggested. They develop a strategy for energy management with day-ahead demand response and power consumption forecasting in SG.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management

et al. 2021

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Existing power grids (PGs) and in-home energy management controllers do not offer its users the choice to maintain comfort and provide a bearable solution in terms of low cost and reduced carbon emission. This work is based on energy usage scheduling and management under electric utility and renewable energy sources i.e., solar energy (SE), controllable heat and power (CHP) and wind energy (WE) together. Efficient integration of renewable energy sources (RES) and battery storage system (BSS) have been suggested to solve the energy management problem, reduce the bill cost, peak-to-average ratio (PAR) and carbon emission. User's electricity bill reduction have been achieved by proposed power usage scheduling method and integrating low cost RESs. PAR minimization have been achieved through shifting the demand in response to real time price from high-peak hours to low-peak hours. In this context, load scheduling and energy storage system management controller (LSEMC) is proposed which is based on heuristic algorithms i.e., genetic algorithm (GA), wind driven optimization (WDO), binary particle swarm optimization (BPSO), bacterial foraging optimization (BFO) and our suggested hybrid of GA, WDO and PSO (HGPDO) algorithm. The performance of the heuristic algorithms and proposed scheme is evaluated numerically. Results demonstrate that our proposed algorithm and the LSEMC reduces the electricity bill, PAR and CO 2 in Case 1, by 58.69%, 52.78% and 72.40%, in Case 2, by 47.55%, 45.02% and 92.90% and in Case 3, by 33.6%, 54.35% and 91.64%, respectively as compared with unscheduled. Moreover, the user comfort by our proposed HGPDO algorithm in terms of delay, thermal, air quality and visual improves by 35.55%, 16.66%, 91.64% and 45%, respectively. INDEX TERMS Energy management; battery energy storage systems; renewable; hybrid heuristic algorithms; power usage scheduling; smart grid.

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Section: Related Workmentioning

confidence: 99%

An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management

et al. 2021

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“…The objective was to minimize cost, however, RESs were not utilized. Authors in [37] proposed a framework based on HEMC. They have also proposed a technique day-ahead grey wolf modified enhanced differential evolution algorithm (DA-GmEDE) to reduce the PAR and electricity bill.…”

Section: Related Workmentioning

confidence: 99%

Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid

Imran¹,

Hafeez²,

Khan³

et al. 2020

IEEE Access

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An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, 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). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling.

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“…The result confirms enhancement over the existing classification-based method. In reference [ 37 ], the authors have proposed a hybrid scheme incorporating an artificial neural network for the prediction model and use forthcoming hours-modified, grey wolf upgraded, differential evolution algorithm to utilize the design of an energy management controller. The prediction model forecasts the price signal using the DR strategy, and the controller schedules the household appliances against the forecasted price and energy patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning

Diyan

Khan

Silva

et al. 2020

Sensors

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A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart things in a smart home environment. Human activities comprise expected and unexpected behavior events; therefore, detecting these events consisting of mutual dependent activities poses a key challenge in the activities detection paradigm. Besides, the battery-powered sensor ubiquitously and extensively monitors activities, disputes, and sensor energy depletion. Therefore, to address these challenges, we propose an Energy and Event Aware-Sensor Duty Cycling scheme. The proposed model predicts the future expected event using the Bi-Directional Long-Short Term Memory model and allocates Predictive Sensors to the predicted event. To detect the unexpected events, the proposed model localizes a Monitor Sensor within a cluster of Hibernate Sensors using the Jaccard Similarity Index. Finally, we optimize the performance of our proposed scheme by employing the Q-Learning algorithm to track the missed or undetected events. The simulation is executed against the conventional Machine Learning algorithms for the sensor duty cycle, scheduling to reduce the sensor energy consumption and improve the activity detection accuracy. The experimental evaluation of our proposed scheme shows significant improvement in activity detection accuracy from 94.12% to 96.12%. Besides, the effective rotation of the Monitor Sensor significantly improves the energy consumption of each sensor with the entire network lifetime.

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An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network

Cited by 74 publications

References 44 publications

An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management

An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management

Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid

Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning

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