Publications by Amirreza Heidari

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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-obje ...

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 stoch ...

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 bu ...

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 servi ...

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 oc ...

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 ...

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 ...

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 ...

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 ...

Traditional control approaches heavily rely on hard-coded expert knowledge, complicating the development of optimal control solutions as system complexity increases. Deep Reinforcement Learning (DRL) offers a selflearning control solution, proving advantag ...

Amirreza Heidari

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Optimal design and operation of a building energy hub: A comparison of exergy-based and energy-based optimization in Swiss and Italian case studies

Adaptive hot water production based on Supervised Learning

Reinforcement Learning for proactive operation of residential energy systems by learning stochastic occupant behavior and fluctuating solar energy: Balancing comfort, hygiene and energy use

Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition

An occupant-centric control framework for balancing comfort, energy use and hygiene in hot water systems: A model-free reinforcement learning approach

DeepValve: Development and experimental testing of a Reinforcement Learning control framework for occupant-centric heating in offices

Reinforcement Learning for the occupant-centric operation of building energy systems: Theoretical and experimental investigations

Combined desiccant-ejector cooling system assisted by Organic Rankine Cycle for zero-power cooling and dehumidification

Water-energy nexus approach to compare performance of evaporative and vapor-compression cooling systems

A trustworthy reinforcement learning framework for autonomous control of a large-scale complex heating system: Simulation and field implementation