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Citations since 2017
20 Research Items
Space heating controls in offices usually follow static schedules detached from actual occupancy, which results in energy waste by unnecessarily heating vacant offices. The uniqueness of stochastic occupancy profile and thermal response time of each office are two main challenges in hard-programming a transferrable control logic that can adapt spac...
Occupant behavior, defined as the presence and energy-related actions of occupants, is today known as a key driver of building energy use. Closing the gap between what is provided by building energy systems and what is actually needed by occupants requires a deeper understanding and consideration of the human factor in the building operation. Howev...
When it comes to residential buildings, there are several stochastic parameters, such as renewable energy production, outdoor air conditions, and occupants' behavior, that are hard to model and predict accurately, with some being unique in each specific building. This increases the complexity of developing a generalizable optimal control method tha...
Occupants’ behavior is a major source of uncertainty for the optimal operation of building energy systems. The highly stochastic hot water use behavior of occupants has led to conservative operational strategies for hot water systems, that try to ensure occupants’ comfort by following energy-intensive operational approaches. Intending to integrate...
A major challenge in the operation of water heating systems lies in the highly stochastic nature of occupant behavior in hot water use, which varies over different buildings and can change over the time. However, the current operational strategies of water heating systems are detached from occupant behavior, and follow a conservative and energy int...
A major challenge in the operation of water heating systems lies in the highly stochastic nature of occupant behavior in hot water use, which varies over different buildings and can change over time. However, the current operational strategies of water heating systems are detached from occupant behavior and follow a conservative and energy-intensiv...
With growing concerns on global warming, exergy-based design methods for energy hubs (EHs) in the urban context have been recently investigated to promote more rational and efficient use of energy sources. This study aims to compare exergy-based multi-objective optimization for energy hubs with two primary energy-based methods. The comparison has b...
A substantial corpus of research has shown that occupancy-related factors, such as presence/absence and movement of occupants, significantly influence energy use and the indoor environmental quality in buildings. Targeting reliable occupancy information is, therefore, a key to achieving efficient HVAC system operations and building management syste...
A major challenge in the common approach of hot water generation in residential houses lies in the highly stochastic nature of domestic hot water (DHW) demand. Learning hot water use behavior enables water heating systems to continuously adapt to the stochastic demand and reduce energy consumption. This paper aims to understand how machine learning...
With improved insulation of building envelopes and the use of low-temperature space heating systems, the share of energy use for domestic hot water (DHW) production in buildings has increased significantly, and nearly become the most energy-expensive service in modern buildings. Early prediction of the energy use for DHW is required for many advanc...
This study presents a novel set-up for desiccant-based cooling and dehumidification systems. In this cycle, a solid desiccant and conventional combined cooling and power (CCP) systems, based on the ejector refrigeration cycle (ERC) and the organic Rankine cycle (ORC), are integrated to provide dehumidification and cooling, simultaneously. The ERC i...
Wet cooling towers are one of the most water-intensive technologies, which are widely used in air conditioning applications, especially in dry regions. Considering the current water crisis around the world, it is essential to improve the design of these cooling towers to reduce their water consumption, while maintaining their cooling performance. M...
Evaporative cooling systems consume high water, but low electricity for their operation. On the other hand, vapor compression systems consume no water, but high electricity. Water and energy systems are interconnected at different levels. Therefore, water use causes an off-site electricity use, and also electricity use results to off-site wat...
Desiccant-based evaporative cooling (DEC) systems are considered as energy efficient alternative to the conventional vapor compression systems in humid climates. A novel hybrid DEC system is presented here to utilize low-grade heat source. In order to evaluate the performance of the system under subtropical humid climates, a dynamic hourly simulati...
Wet cooling towers are one of the most water intensive technologies which are widely used in energy systems. Considering the current water crisis around the world, it is essential to improve the design of these cooling towers to reduce their water consumption while maintaining their cooling performance. Due to the wide use of cooling towers in ener...
Desiccant-based evaporative cooling (DEC) systems provide cooling in humid regions with much lower energy consumption than conventional cooling systems. This energy saving potential could be more significant when a waste heat source is utilized to operate the system. This paper presents a novel configuration for DEC systems utilizing flare stack wa...
Several supervised learning models were proposed in our paper to predict the hot water use behavior of occupants. All the Python codes and the dataset are shared publicly to facilitate the reproduction of models and future research in this field:
Python codes on GitHub:
Dataset (monitored by Thinus Booysen et al as referenced on the excel file):
The number of citations in my google scholar profile is always updated, while in my research gate profile is not, and it is always less.
I am familiar with LSTM neural networks which work well for energy demand forecasting. However, I would like to compare its performance with the two other machine learning methods. What are the best options to choose?
I am going to develop some machine learning models, starting from Artificial Neural Networks. I have seen many papers on MATLAB, PYTHON and some other tools. Which software is easier to use, with more available ready-to-use libraries? Specifically I doubt between MATLAB and PYTHON.
They are published in "Energy Conversion and Management" and "International Journal of Refrigeration"
I have seen interesting studies on energy which use Machine Learning algorithms. As I have a mechanical engineering background I am not sure if I can learn and use machine learning. Is it required to have a computer science background? And are the available tools for machine learning easy to use by people from other disciplines?
I am going to find the optimal heat and mass integration in a plant. In this plant different streams have variable flow rate over time. I am going to determine the optimum heat exchange network and mass integration between all the streams. Is there any module of aspen to do that?
I need to know what is the required quality for different kinds of water demand in a building, like drinking, shower,...A quality index for domestic water and the required amount for every application is very helpful. What is the index that I should search for, and is there any reference for that?
I am going to find the hourly water demand of a building divided to different applications like shower, kitchen, etc. It is very dependent to the users, however, I need a stimation of hourly profile to make a design.
Simulink provides a good environment for dynamic simulation of different process plants. However, I am going to do a dynamic simulation of a process plant and then optimize its configuration and operation. For example, I would like to insert different options for a technology in Simulink, and then do a multi-objective optimization to choose best technologies between different options and to also choose the optimal operation conditions of plant.
In a large scale optimization problem there are many variables, constrains, parameters, etc which is difficult to write all of them in one MATLAB script. I would prefer to use a more convenient interface to solve them in MATLAB. I would be grateful if you propose me a free toolbox or add-on for MATLAB to use for large-scale optimizations.
I would like to know what is the minimum flow rate of the regeneration air flow rate in a solid desiccant wheel for a given flow rate of process air? For example can we use a flow rate half of the process flow rate?