Despite the increasing capabilities of information technologies for data acquisition and processing, building energy management systems still require manual configuration and supervision to achieve optimal performance. Model predictive control (MPC) aims to leverage equipment control-particularly heating, ventilation, and air conditioning (HVAC)-by using a model of the building to capture its dynamic characteristics and to predict its response to alternative control scenarios. Usually, MPC approaches are based on simplified linear models, which support faster computation but also present some limitations regarding interpretability, solution diversification, and longer-term optimization. In this paper, we propose a novel MPC algorithm that uses a full-complexity grey-box simulation model to optimize HVAC operation in non-residential buildings. Our system generates hundreds of candidate operation plans, typically for the next day, and evaluates them in terms of consumption and comfort by means of a parallel simulator configured according to the expected building conditions (weather and occupancy). The system has been implemented and tested in an office building in Helsinki, both in a simulated environment and in the real building, yielding energy savings around 35% during the intermediate winter season and 20% in the whole winter season with respect to the current operation of the heating equipment. INDEX TERMS Model predictive control, simulation, control, building energy management system.
District energy systems, especially those integrating renewables or low exergy sources, have multiple elements for generating heating and cooling. Some of these elements might be used for both purposes: heating and cooling, either simultaneously or alternatively. This makes it more complex to separate the assessment and have a clear picture on performance of cooling service on one side, and heating services on the other, in terms of energy, environmental, and economic results. However, a correct comparison between different district energy configurations or among district energy and conventional solutions requires split assessment of each service. The paper presents a methodology for calculating different district heating and cooling system key performance indicators (KPIs), distinguishing between heating and cooling ones. A total of eleven indicators are organized under four categories: energy, environment, economy and socio-economy. Each KPI is defined for heating service and for cooling service. According to this, the methodology proposes a demand-based and an investment-based share factors that facilitate the heating and cooling KPI calculation.
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