Browsing by Author "Mera, Ana"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Decision support system for distributed energy resources and efficient utilisation of energy in buildings(2013) Perea, Eugenio; Mera, Ana; Siddiqui, Afzal; Heydari, Somayeh; Stadler, Michael; Groissböck, Markus; Alvarez, Angel; DIGITAL ENERGY; LABORATORIO DE TRANSFORMACIÓN URBANADeregulation of energy sectors provides challenges and opportunities alike for operators of public buildings. Exposure to energy prices and CO2 emissions restrictions create incentives to adopt more energy-efficient technologies. Yet, market and technological uncertainties necessitate decision support for risk management. We present a decision support system (DSS) being developed for integrated management of energy efficient buildings. This tool intends to provide decision support for building operators to help them meet their needs in a more efficient, less costly, and less CO2 intensive manner. This paper gives an overview of the strategic DSS module and focuses how the operation DSS module has been conceived starting from mathematical formulation and ending with a validation exercise in a laboratory building.Item An energy-efficient predictive control for HVAC systems applied to tertiary buildings based on regression techniques(2017-10-01) Manjarres, Diana; Mera, Ana; Perea, Eugenio; Lejarazu, Adelaida; Gil-Lopez, Sergio; IA; LABORATORIO DE TRANSFORMACIÓN URBANA; DIGITAL ENERGYHeating ventilation and air conditioning (HVAC) systems represent an important amount of the total energy use in office buildings, accounting for near 30%. Moreover, in countries affected by extreme climates HVAC systems’ contribution to energy demand increases up to 50%. Therefore, the automation of energy efficient strategies that act on the Building Energy Management System (BEMS) in order to improve building energy use becomes increasingly relevant. This paper delves into the devising of a novel HVAC optimization framework, coined as Next24h-Energy, which consists on a two-way communication system, an enhanced database management system and a set of machine learning algorithms based on random forest (RF) regression techniques mainly focused on providing an energy-efficient predictive control of the HVAC system. Therefore, the proposed framework achieves optimal HVAC ON/OFF and mechanical ventilation (MV) schedule operation that minimizes the energy consumption while keeps the building between a predefined indoor temperature margins. Simulation results assess the performance of the proposed Next 24 h-Energy framework at a real office building named Mikeletegi 1 (M1) in Donostia-San Sebastian (Spain) yielding to excellent results and significant energy savings by virtue of its capability of adapting the parameters that control the HVAC schedule in a daily basis without affecting user comfort conditions. Specifically, the energy reduction for the test period is estimated in 48% for the heating and 39% for the cooling consumption.Item Optimizing building energy operations via dynamic zonal temperature settings(2014-03-01) Groissböck, Markus; Heydari, Somayeh; Mera, Ana; Perea, Eugenio; Siddiqui, Afzal S.; Stadler, Michael; LABORATORIO DE TRANSFORMACIÓN URBANA; DIGITAL ENERGYDeregulation of the energy sector has created new markets for producers as well as opportunities for consumers to meet their needs in a more customized way. Yet, traditional building energy management systems operate statically by adjusting air or water flow in heating and cooling systems in response to predetermined triggers, in relation to large deviations in the zone temperature from the equipment's set-point temperature. The writers provide decision support to managers of buildings through dynamic control of the installed equipment that seeks to minimize energy costs. Assuming that the building's occupants have comfort preferences expressed by upper and lower limits for the temperature, the writers model the effect of active equipment control (through changes to either the set point or valve flow) on the zone temperature, taking into account the external temperature, solar gains, building's shell, and internal loads. The energy required to change the zone temperature in each time period is then used to calculate the energy cost in the objective function of an optimization problem. By implementing the model for actual public buildings, the writers demonstrate the advantages of more active equipment-management in terms of lower costs and energy consumption.