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206. Contributors

Author

Bag, Rajib, primary, Bhattacharyya, Siddhartha, additional, Borah, Samarjeet, additional, Borkotokey, Surajit, additional, Chakraborty, Koyel, additional, Dans-Álvarez-de-Sotomayor, Isabel, additional, Barbero, J. Del Olmo, additional, dos Reis Rezende, Pedro Henrique, additional, Fernández Muñoz, C., additional, García-Galera, M.C., additional, Ghatak, Anirban, additional, Gochhayat, Sarada Prasad, additional, Gogoi, Loyimee, additional, González-Sanmamed, Mercedes, additional, Halgamuge, Malka N., additional, Hassanien, Aboul Alla, additional, Hedayatifar, Leila, additional, Kumar, Sachin, additional, Mishra, Brojo Kishore, additional, Moses, Beulah, additional, Mukherjee, Diganta, additional, Muñoz-Carril, Pablo-César, additional, Nguyen, Gia Nhu, additional, Panigrahi, Ranjit, additional, Pati, Bibudhendu, additional, Pattanayak, Binod Kumar, additional, Prasad, Mukesh, additional, Rath, Mamata, additional, Ray, Abhishek, additional, Sadri, Arif Mohaimin, additional, Singh, Amandeep, additional, Singh, Jagendra, additional, Tiwari, Prayag, additional, Tripathy, B.K., additional, Ukkusuri, Satish V., additional, and Yadav, Pranay, additional

Published
2019
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223. Introduction

Author

Bhattacharyya, Siddhartha, Maulik, Ujjwal, Bhattacharyya, Siddhartha, and Maulik, Ujjwal

Published
2013
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235. Predicting Short Term Energy Demand in Smart Grid: A Deep Learning Approach for Integrating Renewable Energy Sources in Line with SDGs 7, 9, and 13

Author

Miah, Md Saef Ullah, Sulaiman, Junaida, Islam, Md. Imamul, Masuduzzaman, Md., Giri, Nimay Chandra, Bhattacharyya, Siddhartha, Favi, Segbedji Geraldo, and Mrsic, Leo

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Machine Learning (cs.LG)
Abstract

Integrating renewable energy sources into the power grid is becoming increasingly important as the world moves towards a more sustainable energy future in line with SDG 7. However, the intermittent nature of renewable energy sources can make it challenging to manage the power grid and ensure a stable supply of electricity, which is crucial for achieving SDG 9. In this paper, we propose a deep learning-based approach for predicting energy demand in a smart power grid, which can improve the integration of renewable energy sources by providing accurate predictions of energy demand. Our approach aligns with SDG 13 on climate action, enabling more efficient management of renewable energy resources. We use long short-term memory networks, well-suited for time series data, to capture complex patterns and dependencies in energy demand data. The proposed approach is evaluated using four historical short-term energy demand data datasets from different energy distribution companies, including American Electric Power, Commonwealth Edison, Dayton Power and Light, and Pennsylvania-New Jersey-Maryland Interconnection. The proposed model is also compared with three other state-of-the-art forecasting algorithms: Facebook Prophet, Support Vector Regression, and Random Forest Regression. The experimental results show that the proposed REDf model can accurately predict energy demand with a mean absolute error of 1.4%, indicating its potential to enhance the stability and efficiency of the power grid and contribute to achieving SDGs 7, 9, and 13. The proposed model also has the potential to manage the integration of renewable energy sources in an effective manner.

Published
2023
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236. Development of an Interactive Anatomical Three-Dimensional Eye Model

Author

Allen, Lauren K., Bhattacharyya, Siddhartha, and Wilson, Timothy D.

Abstract

The discrete anatomy of the eye's intricate oculomotor system is conceptually difficult for novice students to grasp. This is problematic given that this group of muscles represents one of the most common sites of clinical intervention in the treatment of ocular motility disorders and other eye disorders. This project was designed to develop a digital, interactive, three-dimensional (3D) model of the muscles and cranial nerves of the oculomotor system. Development of the 3D model utilized data from the Visible Human Project (VHP) dataset that was refined using multiple forms of 3D software. The model was then paired with a virtual user interface in order to create a novel 3D learning tool for the human oculomotor system. Development of the virtual eye model was done while attempting to adhere to the principles of cognitive load theory (CLT) and the reduction of extraneous load in particular. The detailed approach, digital tools employed, and the CLT guidelines are described herein.

Published
2015
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