Publications de Araz Ashouri | EPFL Graph Search

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In order to fully exploit the potential of renewable energy resources (RERs) for building applications, opti- mal design and control of the different energy systems is a compelling challenge to address. This paper presents a two-step multi-objective optimi ...

This paper presents a flexible and modular control scheme based on distributed model predictive control (DMPC) to achieve optimal operation of decentralized energy systems in smart grids. The proposed approach is used to coordinate multiple distributed ene ...

A multi-objective, multi-period model for optimizing the design and operating strategy of district energy systems is proposed by authors [1]. In the developed model the process and energy integration tech- niques are principally investigated. In the presen ...

A large share of the energy is consumed in buildings but at the individual scale, the energy consumption is rarely optimized; as a consequence, energy is wasted, electricity consumption is characterized by peaks and renewable energy integration is hindered ...

Under Energy Strategy 2050, the federal government of Switzerland plans to greatly reduce per capita energy consumption, decrease the share of fossil fuels, and replace the nuclear electricity generation by gains in efficiency and use of renewable energy s ...

The integration of weather forecasts and demand prediction into the energy management system of buildings is usually achieved using a model-based predictive control. The performance of such control techniques strongly depends on the accuracy of the thermal ...

Araz Ashouri

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Model-based optimization of distributed and renewable energy systems in buildings

Distributed model predictive control of energy systems in microgrids

Multi-objective, multi-period optimization of district energy systems: IV – A case study

Estimating the potential for thermal load management in buildings at a large scale: overcoming challenges towards a replicable methodology

Towards energy-autonomous cities using CO2 networks and Power to Gas storage

Particle Swarm Optimization and Kalman Filtering for Demand Prediction of Commercial Buildings