Engineering Commons^™

Deep Neural Network For Load Forecasting Centred On Architecture Evolution, Santiago Gomez-Rosero, Miriam A M Capretz, London Hydro

Electrical and Computer Engineering Publications

Nowadays, electricity demand forecasting is critical for electric utility companies. Accurate residential load forecasting plays an essential role as an individual component for integrated areas such as neighborhood load consumption. Short-term load forecasting can help electric utility companies reduce waste because electric power is expensive to store. This paper proposes a novel method to evolve deep neural networks for time series forecasting applied to residential load forecasting. The approach centres its efforts on the neural network architecture during the evolution. Then, the model weights are adjusted using an evolutionary optimization technique to tune the model performance automatically. Experimental results on …

Predicting Residential Energy Consumption Using Wavelet Decomposition With Deep Neural Network, Dagimawi Eneyew, Miriam A M Capretz, Girma Bitsuamlak, London Hydro

Electrical and Computer Engineering Publications

Electricity consumption is accelerating due to economic and population growth. Hence, energy consumption prediction is becoming vital for overall consumption management and infrastructure planning. Recent advances in smart electric meter technology are making high-resolution energy consumption data available. However, many parameters influencing energy consumption are not typically monitored for residential buildings. Therefore, this study’s main objective is to develop a data-driven energy consumption forecasting model (next-hour consumption) for residential houses solely based on analyzing electricity consumption data. This research proposes a deep neural network architecture that combines stationary wavelet transform features and convolutional neural networks. The proposed approach utilizes automatically …

The Role Of Blockchain Technologies In Power Markets, David Bowker, Fazel Mohammadi, Abdulraheem Al Gami, Alex Bakakat, Andres Alonso, Bhawna Kapoor, Davor Bosnjak, Edileu Cardoso, Erik Cuadros, Gowri Rajappan, Greg Thorpe, Hannah Davis, Hannes Agabus, Hero Morales, Jelena Milosavljevic, Juan Duran Hernandez, Julian Betancur, Marcelo De Araujo, Nasser Al-Shahrani, Toshiyuki Sawa, Umit Cali, Victor Francisco, Victor Tan, Vijay Vadlamudi, Vladislav Berezovsky, Yousef Almarwan, Yulia Zhilkina

Electrical and Computer Engineering Publications

Blockchain technology offers some significant potential benefits for improving electricity markets. This TB reviews the potential applications for blockchain as energy markets transform. The WG also assessed 37 projects which have already implemented blockchain into energy markets to understand the current level of actual implementation and the benefits in the real world.

Short-Term Load Forecasting Of Microgrid Via Hybrid Support Vector Regression And Long Short-Term Memory Algorithms, Arash Moradzadeh, Sahar Zakeri, Maryam Shoaran, Behnam Mohammadi-Ivatloo, Fazel Mohammadi

Electrical and Computer Engineering Publications

© 2020 by the authors. Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target …

Noisy Importance Sampling Actor-Critic: An Off-Policy Actor-Critic With Experience Replay, Miriam A M Capretz, Norman Tasfi

Electrical and Computer Engineering Publications

This paper presents Noisy Importance Sampling Actor-Critic (NISAC), a set of empirically validated modifications to the advantage actor-critic algorithm (A2C), allowing off-policy reinforcement learning and increased performance. NISAC uses additive action space noise, aggressive truncation of importance sample weights, and large batch sizes. We see that additive noise drastically changes how off-sample experience is weighted for policy updates. The modified algorithm achieves an increase in convergence speed and sample efficiency compared to both the on-policy actor-critic A2C and the importance weighted off-policy actor-critic algorithm. In comparison to state-of-the-art (SOTA) methods, such as actor-critic with experience replay (ACER), NISAC nears the …

Optimal Power Flow Incorporating Facts Devices And Stochastic Wind Power Generation Using Krill Herd Algorithm, Arsalan Abdollahi, Ali Asghar Ghadimi, Mohammad Reza Miveh, Fazel Mohammadi, Francisco Jurado

Electrical and Computer Engineering Publications

© 2020 by the authors. This paper deals with investigating the Optimal Power Flow (OPF) solution of power systems considering Flexible AC Transmission Systems (FACTS) devices and wind power generation under uncertainty. The Krill Herd Algorithm (KHA), as a new meta‐heuristic approach, is employed to cope with the OPF problem of power systems, incorporating FACTS devices and stochastic wind power generation. The wind power uncertainty is included in the optimization problem using Weibull probability density function modeling to determine the optimal values of decision variables. Various objective functions, including minimization of fuel cost, active power losses across transmission lines, emission, …

Integration Of High Voltage Ac/Dc Grids Into Modern Power Systems, Fazel Mohammadi

Electrical and Computer Engineering Publications

© 2020 by the author. The Special Issue on "Integration of High Voltage AC/DC Grids into Modern Power Systems" is published. A total of five qualified papers are published in this Special Issue. The topics of the papers are control, protection, operation, planning, and scheduling of high voltage AC/DC grids. Twenty-five researchers have participated in this Special Issue. We hope that this Special Issue is helpful for high voltage applications.

A Dual Approach For Positive T–S Fuzzy Controller Design And Its Application To Cancer Treatment Under Immunotherapy And Chemotherapy, Elham Ahmadi, Jafar Zarei, Roozbeh Razavi-Far, Mehrdad Saif

Electrical and Computer Engineering Publications

This study proposes an effective positive control design strategy for cancer treatment by resorting to the combination of immunotherapy and chemotherapy. The treatment objective is to transfer the initial number of tumor cells and immune–competent cells from the malignant region into the region of benign growth where the immune system can inhibit tumor growth. In order to achieve this goal, a new modeling strategy is used that is based on Takagi–Sugeno. A Takagi-Sugeno fuzzy model is derived based on the Stepanova nonlinear model that enables a systematic design of the controller. Then, a positive Parallel Distributed Compensation controller is proposed …

Integration Of Ac/Dc Microgrids Into Power Grids, Fazel Mohammadi

Electrical and Computer Engineering Publications

© 2020 by the authors. The Special Issue on "Integration of AC/DC Microgrids into Power Grids" is published. A total of six qualified papers are published in this Special Issue. The topics of the papers are the Optimal Power Flow (OPF), control, protection, and the operation of hybrid AC/DC microgrids. Nine researchers participated in this Special Issue. We hope that this Special Issue is helpful for sustainable energy applications.

Multi-Objective Optimal Reactive Power Planning Under Load Demand And Wind Power Generation Uncertainties Using Ε-Constraint Method, Amir Hossein Shojaei, Ali Asghar Ghadimi, Mohammad Reza Miveh, Fazel Mohammadi, Francisco Jurado

Electrical and Computer Engineering Publications

© 2020 by the authors. This paper presents an improved multi-objective probabilistic Reactive Power Planning (RPP) in power systems considering uncertainties of load demand and wind power generation. The proposed method is capable of simultaneously (1) reducing the reactive power investment cost, (2) minimizing the total active power losses, (3) improving the voltage stability, and (4) enhancing the loadability factor. The generators' voltage magnitude, the transformer's tap settings, and the output reactive power of VAR sources are taken into account as the control variables. To solve the probabilistic multi-objective RPP problem, the "-constraint method is used. To test the effectiveness …

Optimal Placement Of Tcsc For Congestion Management And Power Loss Reduction Using Multi-Objective Genetic Algorithm, Thang Trung Nguyen, Fazel Mohammadi

Electrical and Computer Engineering Publications

© 2020 by the authors. Electricity demand has been growing due to the increase in the world population and higher energy usage per capita as compared to the past. As a result, various methods have been proposed to increase the efficiency of power systems in terms of mitigating congestion and minimizing power losses. Power grids operating limitations result in congestion that specifies the final capacity of the system, which decreases the conventional power capabilities between coverage areas. Flexible AC Transmission Systems (FACTS) can help to decrease flows in heavily loaded lines and lead to lines loadability improvements and cost reduction. …

Optimal Scheduling Of Large-Scale Wind-Hydro-Thermal Systems With Fixed-Head Short-Term Model, Thang Trung Nguyen, Ly Huu Pham, Fazel Mohammadi, Le Chi Kien

Electrical and Computer Engineering Publications

© 2020 by the authors. In this paper, a Modified Adaptive Selection Cuckoo Search Algorithm (MASCSA) is proposed for solving the Optimal Scheduling of Wind-Hydro-Thermal (OSWHT) systems problem. The main objective of the problem is to minimize the total fuel cost for generating the electricity of thermal power plants, where energy from hydropower plants and wind turbines is exploited absolutely. The fixed-head short-term model is taken into account, by supposing that the water head is constant during the operation time, while reservoir volume and water balance are constrained over the scheduled time period. The proposed MASCSA is compared to other …

A Blockchain Approach To Social Responsibility, Augusto Bedin, Wander Queiroz, Miriam A M Capretz, London Hydro

Electrical and Computer Engineering Publications

As blockchain technology matures, more sophisticated solutions arise regarding complex problems. Blockchain continues to spread towards various niches such as government, IoT, energy, and environmental industries. One often overlooked opportunity for blockchain is the social responsibility sector. Presented in this paper is a permissioned blockchain model that enables enterprises to come together and cooperate to optimize their environmental and societal impacts. This is made possible through a private or permissioned blockchain. Permissioned blockchains are blockchain networks where all the participants are known and trust relationships among them can be fostered more smoothly. An example of what a permissioned blockchain would …

A Lightweight Magnetorheological Actuator Using Hybrid Magnetization, Masoud Moghani, Mehrdad Kermani Ph.D., P.Eng.

Electrical and Computer Engineering Publications

Copyright © 2020, IEEE

This paper presents the design and validation of a lightweight Magneto-Rheological (MR) clutch, called Hybrid Magneto-Rheological (HMR) clutch. The clutch utilizes a hybrid magnetization using an electromagnetic coil and a permanent magnet. The electromagnetic coil can adjust the magnetic field
generated by the permanent magnet to a desired value, and fully control the transmitted torque. To achieve the maximum torque to mass ratio, the design of HMR clutch is formulated as a multiobjective optimization problem with three design objectives, namely the transmitted torque, the mass of the clutch, and the
magnetic field strength within the clutch …

Water Conservation Potential Of Self-Funded Foam-Based Flexible Surface-Mounted Floatovoltaics, Koami Soulemane Hayibo, Pierce Mayville, Ravneet Kaur Kailey, Joshua M. Pearce

Electrical and Computer Engineering Publications

A potential solution to the coupled water–energy–food challenges in land use is the concept of floating photovoltaics or floatovoltaics (FPV). In this study, a new approach to FPV is investigated using a flexible crystalline silicon-based photovoltaic (PV) module backed with foam, which is less expensive than conventional pontoon-based FPV. This novel form of FPV is tested experimentally for operating temperature and performance and is analyzed for water-savings using an evaporation calculation adapted from the Penman–Monteith model. The results show that the foam-backed FPV had a lower operating temperature than conventional pontoon-based FPV, and thus a 3.5% higher energy output per …

An Improved Mixed Ac/Dc Power Flow Algorithm In Hybrid Ac/Dc Grids With Mt-Hvdc Systems, Fazel Mohammadi, Gholam Abbas Nazri, Mehrdad Saif

Electrical and Computer Engineering Publications

© 2019 by the authors. One of the major challenges on large-scale Multi-Terminal High Voltage Direct Current (MT-HVDC) systems is the steady-state interaction of the hybrid AC/DC grids to achieve an accurate Power Flow (PF) solution. In PF control of MT-HVDC systems, different operational constraints, such as the voltage range, voltage operating region, Total Transfer Capability (TTC), transmission reliability margin, converter station power rating, etc. should be considered. Moreover, due to the nonlinear behavior of MT-HVDC systems, any changes (contingencies and/or faults) in the operating conditions lead to a significant change in the stability margin of the entire or several …

An Improved Droop-Based Control Strategy For Mt-Hvdc Systems, Fazel Mohammadi, Gholam Abbas Nazri, Mehrdad Saif

Electrical and Computer Engineering Publications

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This paper presents an improved droop-based control strategy for the active and reactive power-sharing on the large-scale Multi-Terminal High Voltage Direct Current (MT-HVDC) systems. As droop parameters enforce the stability of the DC grid, and allow the MT-HVDC systems to participate in the AC voltage and frequency regulation of the different AC systems interconnected by the DC grids, a communication-free control method to optimally select the droop parameters, consisting of AC voltage-droop, DC voltage-droop, and frequency-droop parameters, is investigated to balance the power in MT-HVDC systems and minimize AC voltage, DC …

Intrinsic Measures And Shape Analysis Of The Intratemporal Facial Nerve, Thomas Hudson, Bradley Gare, Daniel Allen, Hanif Ladak, Sumit Agrawal

Electrical and Computer Engineering Publications

Hypothesis: To characterize anatomical measurements and shape variation of the facial nerve within the temporal bone, and to create statistical shape models (SSMs) to enhance knowledge of temporal bone anatomy and aid in automated segmentation.

Background: The facial nerve is a fundamental structure in otologic surgery, and detailed anatomic knowledge with surgical experience are needed to avoid its iatrogenic injury. Trainees can use simulators to practice surgical techniques, however manual segmentation required to develop simulations can be time consuming. Consequently, automated segmentation algorithms have been developed that use atlas registration, SSMs, and deep learning.

Methods: Forty cadaveric temporal bones were …

Deep Learning For Load Forecasting With Smart Meter Data: Online Adaptive Recurrent Neural Network, Mohammad Navid Fekri, Harsh Patel, Katarina Grolinger, Vinay Sharma

Electrical and Computer Engineering Publications

No abstract provided.

Pwd-3dnet: A Deep Learning-Based Fully-Automated Segmentation Of Multiple Structures On Temporal Bone Ct Scans, Western University, London Health Science Centre

Electrical and Computer Engineering Publications

The temporal bone is a part of the lateral skull surface that contains organs responsible for hearing and balance. Mastering surgery of the temporal bone is challenging because of this complex and microscopic three-dimensional anatomy. Segmentation of intra-temporal anatomy based on computed tomography (CT) images is necessary for applications such as surgical training and rehearsal, amongst others. However, temporal bone segmentation is challenging due to the similar intensities and complicated anatomical relationships among crit- ical structures, undetectable small structures on standard clinical CT, and the amount of time required for manual segmentation. This paper describes a single multi-class deep learning-based …

Edge-Cloud Computing For Iot Data Analytics: Embedding Intelligence In The Edge With Deep Learning, Ananda Mohon M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …