Mohammad Shojafar | Università degli Studi "La Sapienza" di Roma

Mohammad Shojafar

Università degli Studi "La Sapienza" di Roma, Department of Information Engineering, Electronics and Telecommunications, PhD student/ Associated Researcher -CESPOL-

Università degli Studi "La Sapienza" di Roma, Department of Information Engineering, Electronics and Telecommunications, PhD student/ Associated Researcher -CESPOL- |

Artificial Intelligence

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Mohammad Shojafar (S'13-M'17) is an Intel Innovator and senior researcher in SPRITZ Security and Privacy Research Group at the University of Padua, Italy. He was CNIT Senior Researcher at the University of Rome Tor Vergata contributed on European H2020 Superfluidity project. Also, he completed some Italian projects named "SAMMClouds", "V-FoG", "PRIN15" projects aim to address some of the open issues related to the Software as a Service (SaaS) and Infrastructure as a Service (IaaS) systems In Cloud and Fog computing which are supported by the University of Sapienza Rome and University of Modena and Reggio Emilia, Italy, respectively. He received the Ph.D. degree from Sapienza University of Rome, Rome, Italy, in 2016 with an "Excellent" degree.
Supervisors: Mauro Conti, Luca Chiaraviglio, Mukesh Singhal, Rajmukar Buyya, Enzo Baccarelli, and Jemal Abawajy
Address: Department of Mathematics and Computer Science,
University of Padua,
Via Trieste, 63,
35131, Padua, Italy.

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Papers

Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions

by Seyedeh Narjes Fallah and Mohammad Shojafar

Energy management systems are designed to monitor, optimize, and control the smart grid energy ma... more Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand management.

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