Design and Implementation of Smart Meter with Load Forecasting Feature for Residential Customers

Masum, Abdul Kadar Muhammad; Saveed, Md. Hamid; Chy, Md. Kalim Amzad; Hasan, Md. Tanvir; Reza, S M Taslim

doi:10.1109/ecace.2019.8679357

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…In this study, a novel illustration of highlight significance investigation as applied to brilliant meter order for non-private structures. The smart meter information is monstrous, and experts frequently need to utilize worldly knowledge to separate metadata data about structures [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Demand Forecasting Using Fused Machine Learning Approaches

Ghazal¹,

Noreen²,

Said³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to feature-based data provided a better result. Lastly, decision-based fusion applied to both datasets to predict power consumption through smart meters and get better results than previous techniques. The established model smart meter with automatic load control increases the effectiveness of energy management. The proposed EDF-FMLA model achieved 90.70 accuracy for predicting energy consumption with a smart meter which is better than the existing approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Energy Demand Forecasting Using Fused Machine Learning Approaches

Ghazal¹,

Noreen²,

Said³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“… Standard robotics control vs DNN based end-to-end control [ 10 ]. ( a ) standard approach ( b ) deep neural network approach.…”

Section: Figurementioning

confidence: 99%

“…Considering physical and operational factors, another group of researchers [ 5 ] engaged AI to assess pipe break rate and [ 6 ] decoding clinical biomarker space of COVID-19. Nowadays, AI is also broadly used in building the smart city [ 7 , 8 ], smart meter [ 9 , 10 ], agriculture [ 11 , 12 , 13 ], education [ 14 , 15 ], healthcare [ 16 , 17 , 18 ] and so on. Machine learning is a branch of artificial intelligence that allows machines to learn without being explicitly taught from prior data or experiences.…”

Section: Introductionmentioning

confidence: 99%

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

Chy

Masum

Sayeed

et al. 2021

Sensors

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The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install.

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