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The data in this dataset is derived from the study published in https://doi.org/10.1016/j.egyr.2020.11.112 . Data shows hourly heating costs for covering the heating demand of an average dwelling in Madrid (Spain) during 2018-2019 heating season (from 01/10/18 to 31/03/19) through different technologies. A conventional gas boiler and an air-to-air heat pump were selected; additionally, three different electric tariffs were selected: dTOU, RTP and a static tariff. One file is provided as a xlsx file with the following hourly features: Date Hour Tout: Outdoor temperature in ºC RTP: Real Time Price electric tariff in € dTOU: Dynamic Time of Use electric tariff in € E.Flat: Static electric tariff in € Gas: Natural gas tariff in € Demand: Dwellings heating demand in kWh COPR32a: Instant COP of R32 working fluid Con.Elec: Electric consumption of the heat pump in kWh ConGas: Gas boiler consumption in kWh €.elec_RTR: Cost of the electric consumption under the RTP tariff in € €.elec_Flat: Cost of the electric consumption under the static tariff in € €.elec_dTOU: Cost of the electric consumption under the dTOU tariff in € €.gas: Cost of the natural gas consumption in €
Olaia Eguiarte; Pablo De Agustin Camacho; Antonio Garrido-Marijuan. Domestic space heating dynamic costs under different technologies and energy tariffs: Case study in Spain (dataset). 2021, 1 .
AMA StyleOlaia Eguiarte, Pablo De Agustin Camacho, Antonio Garrido-Marijuan. Domestic space heating dynamic costs under different technologies and energy tariffs: Case study in Spain (dataset). . 2021; ():1.
Chicago/Turabian StyleOlaia Eguiarte; Pablo De Agustin Camacho; Antonio Garrido-Marijuan. 2021. "Domestic space heating dynamic costs under different technologies and energy tariffs: Case study in Spain (dataset)." , no. : 1.
The data in this dataset is derived from the study published in https://doi.org/10.1016/j.egyr.2020.11.112 . Data shows hourly heating costs for covering the heating demand of an average dwelling in Madrid (Spain) during 2018-2019 heating season (from 01/10/18 to 31/03/19) through different technologies. A conventional gas boiler and an air-to-air heat pump were selected; additionally, three different electric tariffs were selected: dTOU, RTP and a static tariff. One file is provided as a xlsx file with the following hourly features: Date Hour Tout: Outdoor temperature in ºC RTP: Real Time Price electric tariff in € dTOU: Dynamic Time of Use electric tariff in € E.Flat: Static electric tariff in € Gas: Natural gas tariff in € Demand: Dwellings heating demand in kWh COPR32a: Instant COP of R32 working fluid Con.Elec: Electric consumption of the heat pump in kWh ConGas: Gas boiler consumption in kWh €.elec_RTR: Cost of the electric consumption under the RTP tariff in € €.elec_Flat: Cost of the electric consumption under the static tariff in € €.elec_dTOU: Cost of the electric consumption under the dTOU tariff in € €.gas: Cost of the natural gas consumption in €
Olaia Eguiarte; Pablo De Agustin Camacho; Antonio Garrido-Marijuan. Domestic space heating dynamic costs under different technologies and energy tariffs: Case study in Spain (dataset). 2021, 1 .
AMA StyleOlaia Eguiarte, Pablo De Agustin Camacho, Antonio Garrido-Marijuan. Domestic space heating dynamic costs under different technologies and energy tariffs: Case study in Spain (dataset). . 2021; ():1.
Chicago/Turabian StyleOlaia Eguiarte; Pablo De Agustin Camacho; Antonio Garrido-Marijuan. 2021. "Domestic space heating dynamic costs under different technologies and energy tariffs: Case study in Spain (dataset)." , no. : 1.
The data contained in this file is derived from the study published in https://doi.org/10.3390/en13153939. For each of the studied countries, hourly data of the selected heating season (2018-2019) is presented, which includes: Primary energy, Cost and GHG emissions of a Heat pump and the most common non-electric heating system of the country. One file per country is provided as a xlsx file with the following hourly features: Date Hour Heat demand: Building heating demand in kWh COPR32: Instant COP of the HP with R32 working fluid COPR410a: Instant COP of the HP with R410a working fluid ConElec: Electric consumption of the heat pump in kWh ConNE: Non-electric systems consumption €.elec: Cost of the electric consumption in € €.NE: Cost of the Non-electric systems consumption in € Price difference: Price difference between both systems in € PE.elec: Primary energy of the electricity consumption in KWh PE.NE: Primary energy of the non-electric systems consumption in KWh GHG.elec: GHG emissions of the electricity consumption in kg GHG.NE: GHG emissions of the electricity consumption in kg
Olaia Eguiarte; Pablo de Agustín-Camacho; Antonio Garrido-Marijuan. Energetic, Economic and Environment hourly data of electric and non-electric heating systems for different European countries and UK (dataset). 2021, 1 .
AMA StyleOlaia Eguiarte, Pablo de Agustín-Camacho, Antonio Garrido-Marijuan. Energetic, Economic and Environment hourly data of electric and non-electric heating systems for different European countries and UK (dataset). . 2021; ():1.
Chicago/Turabian StyleOlaia Eguiarte; Pablo de Agustín-Camacho; Antonio Garrido-Marijuan. 2021. "Energetic, Economic and Environment hourly data of electric and non-electric heating systems for different European countries and UK (dataset)." , no. : 1.
The data contained in this file is derived from the study published in https://doi.org/10.3390/en13153939. For each of the studied countries, hourly data of the selected heating season (2018-2019) is presented, which includes: Primary energy, Cost and GHG emissions of a Heat pump and the most common non-electric heating system of the country. One file per country is provided as a xlsx file with the following hourly features: Date Hour Heat demand: Building heating demand in kWh COPR32: Instant COP of the HP with R32 working fluid COPR410a: Instant COP of the HP with R410a working fluid ConElec: Electric consumption of the heat pump in kWh ConNE: Non-electric systems consumption €.elec: Cost of the electric consumption in € €.NE: Cost of the Non-electric systems consumption in € Price difference: Price difference between both systems in € PE.elec: Primary energy of the electricity consumption in KWh PE.NE: Primary energy of the non-electric systems consumption in KWh GHG.elec: GHG emissions of the electricity consumption in kg GHG.NE: GHG emissions of the electricity consumption in kg
Olaia Eguiarte; Pablo de Agustín-Camacho; Antonio Garrido-Marijuan. Energetic, Economic and Environment hourly data of electric and non-electric heating systems for different European countries and UK (dataset). 2021, 1 .
AMA StyleOlaia Eguiarte, Pablo de Agustín-Camacho, Antonio Garrido-Marijuan. Energetic, Economic and Environment hourly data of electric and non-electric heating systems for different European countries and UK (dataset). . 2021; ():1.
Chicago/Turabian StyleOlaia Eguiarte; Pablo de Agustín-Camacho; Antonio Garrido-Marijuan. 2021. "Energetic, Economic and Environment hourly data of electric and non-electric heating systems for different European countries and UK (dataset)." , no. : 1.
The data in this dataset is derived from the one of the pre-validation tests of the HOLISDER European project http://holisder.eu/ . The testing premises consist of an enclosed area in the ground floor of KUBIK by TECNALIA Research & Innovation (a three floors experimental buildings, including an underground basement where central HVAC equipment are located). The ground floor is cooled through two fan-coil, as terminal units, while the cooling generation was performed by two chillers located in the basement. The SCADA system of the building records operation temperatures, mass flows, delivered power, fan-coils operation volume, setpoint temperatures and operation modes. The test was carried out from 01/07/20 to 31/10/2020 under strict cooling comfort boundaries in order to characterize the premise’s systems. One file is provided as a xlsx file with the following features with 10-minute frequency: (Disclaimer: Provided dataset is raw-data and null or error values were not deleted from it) Weather station METEO_RoofSR_Global.PV: Global horizontal radiation METEO_GardenWS_AT.PV: Outdoor dry bulb temperature FANCOIL 1 F0N2_FCM1.Enable: Fancoil1 enabled F0N2_FCM1.PV: Fancoil1 thermostat reading F0N2_FCM1.SP: Fancoil1 thermostat setpoint F0N2_FCM1.ST_COOL_Mode: Fancoil on cooling mode F0N2_FCM1.ST_HOT_Mode: Fancoil on heating mode F0N2_FCM1.ST_VENT_Mode : Fancoil on fan mode F0N2_FCM1_C5.PV: Inlet water flow F0N2_FCM1_ST1.PV: Inlet water temperature F0N2_FCM1_ST2.PV: Outlet water temperature F0N2_FCM1_W5.kWh: Cumulative thermal supply F0N2_FCM1_W5.kWh_AC: Cumulative heat F0N2_FCM1_W5.kWh_AF: Cumulative cold F0N2_FCM1_W5.W: Thermal power FANCOIL 2 F0N2_FCM2.Enable: Fancoil2 enabled F0N2_FCM2.PV: Fancoil2 thermostat reading F0N2_FCM2.SP: Fancoil2 thermostat setpoint F0N2_FCM2.ST_COOL_Mode: Fancoil on cooling mode F0N2_FCM2.ST_HOT_Mode: Fancoil on heating mode F0N2_FCM2.ST_VENT_Mode : Fancoil on fan mode F0N2_FCM2_C6.PV: Inlet water flow F0N2_FCM2_ST1.PV: Inlet water temperature F0N2_FCM2_ST2.PV: Outlet water temperature F0N2_FCM2_W6.kWh: Cumulative thermal supply F0N2_FCM2_W6.kWh_AC: Cumulative heat F0N2_FCM2_W6.kWh_AF: Cumulative cold F0N2_FCM2_W6.W: Thermal power CHILLER 1 HVAC_BP301.OUT: Impulsion pump to distribution order HVAC_CP301.PV: Outlet cold water flow HVAC_ENF1_CVM.Hz: Frequency HVAC_ENF1_CVM.I_III: Average Intensity HVAC_ENF1_CVM.IR: Intensity phase R HVAC_ENF1_CVM.IS: Intensity phase S HVAC_ENF1_CVM.IT: Intensity phase T HVAC_ENF1_CVM.kVArh_in: Reactive Energy (R+) HVAC_ENF1_CVM.kVArh_out: Reactive Energy (R-) HVAC_ENF1_CVM.kWh_in: Active Energy A+) HVAC_ENF1_CVM.kWh_out: Active Energy (A-) HVAC_ENF1_CVM.PF_III: Power factor (average) HVAC_ENF1_CVM.PF_R: Power factor phase R HVAC_ENF1_CVM.PF_S: Power factor phase S HVAC_ENF1_CVM.PF_T: Power factor phase T HVAC_ENF1_CVM.V_III: Combined Voltage HVAC_ENF1_CVM.VA: Apparent Power HVAC_ENF1_CVM.Var: Reactive Power HVAC_ENF1_CVM.VRS: Phases R-S combined voltage HVAC_ENF1_CVM.VST: Phases S-T combined voltage HVAC_ENF1_CVM.VTR: Phases T-R combined voltage HVAC_ENF1_CVM.W: Active Power HVAC_ENF1_m.OUT: Turning on order HVAC_Enfriadora1.SP: Control setpoint HVAC_SP301.PV: Cold water outlet HVAC_SP302.PV: Cold water inlet HVAC_WP301.kWh: Cumulative thermal energy produced (cold) HVAC_WP301.W: Thermal power (cold) CHILLER 2 HVAC_BP401.OUT: Impulsion pump to distribution order HVAC_CP401.PV: Outlet cold water flow HVAC_ENF2_CVM.Hz: Frequency HVAC_ENF2_CVM.I_III: Average Intensity HVAC_ENF2_CVM.IR: Intensity phase R HVAC_ENF2_CVM.IS: Intensity phase S HVAC_ENF2_CVM.IT: Intensity phase T HVAC_ENF2_CVM.kVArh_in: Reactive Energy (R+) HVAC_ENF2_CVM.kVArh_out: Reactive Energy (R-) HVAC_ENF2_CVM.kWh_in: Active Energy A+) HVAC_ENF2_CVM.kWh_out: Active Energy (A-) HVAC_ENF2_CVM.PF_III: Power factor (average) HVAC_ENF2_CVM.PF_R: Power factor phase R HVAC_ENF2_CVM.PF_S: Power factor phase S HVAC_ENF2_CVM.PF_T: Power factor phase T HVAC_ENF2_CVM.V_III: Combined Voltage HVAC_ENF2_CVM.VA: Apparent Power HVAC_ENF2_CVM.Var: Reactive Power HVAC_ENF2_CVM.VRS: Phases R-S combined voltage HVAC_ENF2_CVM.VST: Phases S-T combined voltage HVAC_ENF2_CVM.VTR: Phases T-R combined voltage HVAC_ENF2_CVM.W: Active Power HVAC_ENF2_m.OUT: Turning on order HVAC_Enfriadora2.SP: Control setpoint HVAC_SP401.PV: Cold water outlet HVAC_SP402.PV: Cold water inlet HVAC_WP401.kWh: Cumulative thermal energy produced (cold) HVAC_WP401.W: Thermal power (cold) Chillers room temperature HVAC_STA.PV: Air temperature in the basement where chillers are located (point A) HVAC_STB.PV: Air temperature in the basement where chillers are located (point B) HVAC_STC.PV: Air temperature in the basement where chillers are located (point C) HVAC_STD.PV: Air temperature in the basement where chillers are located (point D)
Pablo De Agustin Camacho; Olaia Eguiarte. Experimental cooling data in KUBIK lab-building during the 2020 season. HOLISDER project pre-pilot testing activities. 2021, 1 .
AMA StylePablo De Agustin Camacho, Olaia Eguiarte. Experimental cooling data in KUBIK lab-building during the 2020 season. HOLISDER project pre-pilot testing activities. . 2021; ():1.
Chicago/Turabian StylePablo De Agustin Camacho; Olaia Eguiarte. 2021. "Experimental cooling data in KUBIK lab-building during the 2020 season. HOLISDER project pre-pilot testing activities." , no. : 1.
The data in this dataset is derived from the one of the pre-validation tests of the HOLISDER European project http://holisder.eu/ . The testing premises consist of an enclosed area in the ground floor of KUBIK by TECNALIA Research & Innovation (a three floors experimental buildings, including an underground basement where central HVAC equipment are located). The ground floor is cooled through two fan-coil, as terminal units, while the cooling generation was performed by two chillers located in the basement. The SCADA system of the building records operation temperatures, mass flows, delivered power, fan-coils operation volume, setpoint temperatures and operation modes. The test was carried out from 01/07/20 to 31/10/2020 under strict cooling comfort boundaries in order to characterize the premise’s systems. One file is provided as a xlsx file with the following features with 10-minute frequency: (Disclaimer: Provided dataset is raw-data and null or error values were not deleted from it) Weather station METEO_RoofSR_Global.PV: Global horizontal radiation METEO_GardenWS_AT.PV: Outdoor dry bulb temperature FANCOIL 1 F0N2_FCM1.Enable: Fancoil1 enabled F0N2_FCM1.PV: Fancoil1 thermostat reading F0N2_FCM1.SP: Fancoil1 thermostat setpoint F0N2_FCM1.ST_COOL_Mode: Fancoil on cooling mode F0N2_FCM1.ST_HOT_Mode: Fancoil on heating mode F0N2_FCM1.ST_VENT_Mode : Fancoil on fan mode F0N2_FCM1_C5.PV: Inlet water flow F0N2_FCM1_ST1.PV: Inlet water temperature F0N2_FCM1_ST2.PV: Outlet water temperature F0N2_FCM1_W5.kWh: Cumulative thermal supply F0N2_FCM1_W5.kWh_AC: Cumulative heat F0N2_FCM1_W5.kWh_AF: Cumulative cold F0N2_FCM1_W5.W: Thermal power FANCOIL 2 F0N2_FCM2.Enable: Fancoil2 enabled F0N2_FCM2.PV: Fancoil2 thermostat reading F0N2_FCM2.SP: Fancoil2 thermostat setpoint F0N2_FCM2.ST_COOL_Mode: Fancoil on cooling mode F0N2_FCM2.ST_HOT_Mode: Fancoil on heating mode F0N2_FCM2.ST_VENT_Mode : Fancoil on fan mode F0N2_FCM2_C6.PV: Inlet water flow F0N2_FCM2_ST1.PV: Inlet water temperature F0N2_FCM2_ST2.PV: Outlet water temperature F0N2_FCM2_W6.kWh: Cumulative thermal supply F0N2_FCM2_W6.kWh_AC: Cumulative heat F0N2_FCM2_W6.kWh_AF: Cumulative cold F0N2_FCM2_W6.W: Thermal power CHILLER 1 HVAC_BP301.OUT: Impulsion pump to distribution order HVAC_CP301.PV: Outlet cold water flow HVAC_ENF1_CVM.Hz: Frequency HVAC_ENF1_CVM.I_III: Average Intensity HVAC_ENF1_CVM.IR: Intensity phase R HVAC_ENF1_CVM.IS: Intensity phase S HVAC_ENF1_CVM.IT: Intensity phase T HVAC_ENF1_CVM.kVArh_in: Reactive Energy (R+) HVAC_ENF1_CVM.kVArh_out: Reactive Energy (R-) HVAC_ENF1_CVM.kWh_in: Active Energy A+) HVAC_ENF1_CVM.kWh_out: Active Energy (A-) HVAC_ENF1_CVM.PF_III: Power factor (average) HVAC_ENF1_CVM.PF_R: Power factor phase R HVAC_ENF1_CVM.PF_S: Power factor phase S HVAC_ENF1_CVM.PF_T: Power factor phase T HVAC_ENF1_CVM.V_III: Combined Voltage HVAC_ENF1_CVM.VA: Apparent Power HVAC_ENF1_CVM.Var: Reactive Power HVAC_ENF1_CVM.VRS: Phases R-S combined voltage HVAC_ENF1_CVM.VST: Phases S-T combined voltage HVAC_ENF1_CVM.VTR: Phases T-R combined voltage HVAC_ENF1_CVM.W: Active Power HVAC_ENF1_m.OUT: Turning on order HVAC_Enfriadora1.SP: Control setpoint HVAC_SP301.PV: Cold water outlet HVAC_SP302.PV: Cold water inlet HVAC_WP301.kWh: Cumulative thermal energy produced (cold) HVAC_WP301.W: Thermal power (cold) CHILLER 2 HVAC_BP401.OUT: Impulsion pump to distribution order HVAC_CP401.PV: Outlet cold water flow HVAC_ENF2_CVM.Hz: Frequency HVAC_ENF2_CVM.I_III: Average Intensity HVAC_ENF2_CVM.IR: Intensity phase R HVAC_ENF2_CVM.IS: Intensity phase S HVAC_ENF2_CVM.IT: Intensity phase T HVAC_ENF2_CVM.kVArh_in: Reactive Energy (R+) HVAC_ENF2_CVM.kVArh_out: Reactive Energy (R-) HVAC_ENF2_CVM.kWh_in: Active Energy A+) HVAC_ENF2_CVM.kWh_out: Active Energy (A-) HVAC_ENF2_CVM.PF_III: Power factor (average) HVAC_ENF2_CVM.PF_R: Power factor phase R HVAC_ENF2_CVM.PF_S: Power factor phase S HVAC_ENF2_CVM.PF_T: Power factor phase T HVAC_ENF2_CVM.V_III: Combined Voltage HVAC_ENF2_CVM.VA: Apparent Power HVAC_ENF2_CVM.Var: Reactive Power HVAC_ENF2_CVM.VRS: Phases R-S combined voltage HVAC_ENF2_CVM.VST: Phases S-T combined voltage HVAC_ENF2_CVM.VTR: Phases T-R combined voltage HVAC_ENF2_CVM.W: Active Power HVAC_ENF2_m.OUT: Turning on order HVAC_Enfriadora2.SP: Control setpoint HVAC_SP401.PV: Cold water outlet HVAC_SP402.PV: Cold water inlet HVAC_WP401.kWh: Cumulative thermal energy produced (cold) HVAC_WP401.W: Thermal power (cold) Chillers room temperature HVAC_STA.PV: Air temperature in the basement where chillers are located (point A) HVAC_STB.PV: Air temperature in the basement where chillers are located (point B) HVAC_STC.PV: Air temperature in the basement where chillers are located (point C) HVAC_STD.PV: Air temperature in the basement where chillers are located (point D)
Pablo De Agustin Camacho; Olaia Eguiarte. Experimental cooling data in KUBIK lab-building during the 2020 season. HOLISDER project pre-pilot testing activities. 2021, 1 .
AMA StylePablo De Agustin Camacho, Olaia Eguiarte. Experimental cooling data in KUBIK lab-building during the 2020 season. HOLISDER project pre-pilot testing activities. . 2021; ():1.
Chicago/Turabian StylePablo De Agustin Camacho; Olaia Eguiarte. 2021. "Experimental cooling data in KUBIK lab-building during the 2020 season. HOLISDER project pre-pilot testing activities." , no. : 1.
Although it has been demonstrated that demand-side flexibility is possible, business application of residential and small tertiary demand response programs has been slow to develop. This paper presents a holistic demand response optimization framework that enables significant energy costs reduction for consumers. Moreover, buildings are introduced as main contributors to balance energy networks. The solution basis consists of a modular interoperability and data management framework that enables open standards-based communication along the demand response value chain. The solution is being validated in four large-scale pilot sites, which have diverse building types, energy systems and energy carriers. Furthermore, they offer diverse climatic conditions, and demographic and cultural characteristics to establish representative results.
Ander Romero-Amorrortu; Pablo De Agustín-Camacho; Olaia Eguiarte; George B. Huitema; Laura Morcillo; Milan Vukovic. HOLISDER Project: Introducing Residential and Tertiary Energy Consumers as Active Players in Energy Markets. Proceedings 2021, 65, 31 .
AMA StyleAnder Romero-Amorrortu, Pablo De Agustín-Camacho, Olaia Eguiarte, George B. Huitema, Laura Morcillo, Milan Vukovic. HOLISDER Project: Introducing Residential and Tertiary Energy Consumers as Active Players in Energy Markets. Proceedings. 2021; 65 (1):31.
Chicago/Turabian StyleAnder Romero-Amorrortu; Pablo De Agustín-Camacho; Olaia Eguiarte; George B. Huitema; Laura Morcillo; Milan Vukovic. 2021. "HOLISDER Project: Introducing Residential and Tertiary Energy Consumers as Active Players in Energy Markets." Proceedings 65, no. 1: 31.
Given the current climate emergency, our planet is suffering. Mitigation measures must be urgently deployed in urban environments, which are responsible for more than 70% of global CO2 emissions. In this sense, a deeper integration between energy and urban planning disciplines is a key factor for effective decarbonisation in urban environments. This is addressed in the Cities4ZERO decarbonisation methodology. This method specifically points out the need for technology-based solutions able to support that integration among both disciplines at a local level, enriching decision-making in urban decarbonisation policy-making, diagnosis, planning, and follow-up tasks, incorporating the spatial dimension to the whole process (GIS-based), as well as the possibilities of the digital era. Accordingly, this paper explores the demands of both integrated urban energy planning and European/Basque energy directives, to set the main requisites and functionalities that Decision Support Systems (DSSs) must fulfil to effectively support city managers and the urban decarbonisation process.
Koldo Urrutia-Azcona; Elena Usobiaga-Ferrer; Pablo De Agustín-Camacho; Patricia Molina-Costa; Mauricia Benedito-Bordonau; Iván Flores-Abascal. ENER-BI: Integrating Energy and Spatial Data for Cities’ Decarbonisation Planning. Sustainability 2021, 13, 383 .
AMA StyleKoldo Urrutia-Azcona, Elena Usobiaga-Ferrer, Pablo De Agustín-Camacho, Patricia Molina-Costa, Mauricia Benedito-Bordonau, Iván Flores-Abascal. ENER-BI: Integrating Energy and Spatial Data for Cities’ Decarbonisation Planning. Sustainability. 2021; 13 (1):383.
Chicago/Turabian StyleKoldo Urrutia-Azcona; Elena Usobiaga-Ferrer; Pablo De Agustín-Camacho; Patricia Molina-Costa; Mauricia Benedito-Bordonau; Iván Flores-Abascal. 2021. "ENER-BI: Integrating Energy and Spatial Data for Cities’ Decarbonisation Planning." Sustainability 13, no. 1: 383.
Dynamic energy tariffs facilitate engaging domestic consumers on demand management, contributing to grid’s stability, but requires of informed decision enabling tools. This paper presents a domestic heating costs calculation method for different heating technologies (gas boiler, heat-pumps) and a range of energy tariffs. Based on physical modeling, effect of outdoor temperature in the COP of heat-pumps is assessed. The methodology is applied to the 2018/19 heating season in Madrid (Spain), calculating the heating costs under four diverse energy tariffs (static gas tariff, static electricity tariff, real-time-price electricity tariff, dynamic time-of-use electricity tariff) for a typical home demand. The hourly results for two representative days are detailed, along with the aggregated results for the whole season. Along the season, the continuous changes in energy wholesale market prices and weather conditions make one heating technology and/or tariff more convenient each time. For the whole season, the dynamic time-of-use tariff considered would imply heating costs up to 40% lower than the static gas tariff. The results are strongly conditioned by climate conditions and national energy market evolutions. Day-ahead information on the actual heating costs might lead to domestic end-users to adapt their behavior and consumption patterns for more cost-effective use of the energy.
O. Eguiarte; P. de Agustín-Camacho; A. Garrido-Marijuán; A. Romero-Amorrortu. Domestic space heating dynamic costs under different technologies and energy tariffs: Case study in Spain. Energy Reports 2020, 6, 220 -225.
AMA StyleO. Eguiarte, P. de Agustín-Camacho, A. Garrido-Marijuán, A. Romero-Amorrortu. Domestic space heating dynamic costs under different technologies and energy tariffs: Case study in Spain. Energy Reports. 2020; 6 ():220-225.
Chicago/Turabian StyleO. Eguiarte; P. de Agustín-Camacho; A. Garrido-Marijuán; A. Romero-Amorrortu. 2020. "Domestic space heating dynamic costs under different technologies and energy tariffs: Case study in Spain." Energy Reports 6, no. : 220-225.
Heat pumps (HP) are an efficient alternative to non-electric heating systems (NEHS), being a cost-effective mean to support European building sector decarbonization. The paper studies HP and NEHS performance in residential buildings, under different climate conditions and energy tariffs, in six different European countries. Furthermore, a primary energy and environmental analysis is performed to evaluate if the use of HPs is more convenient than NEHS, based on different factors of the electric mix in each country. A specific HP model is developed considering the main physical phenomena occurring along its cycle. Open data from building, climatic and economic sources are used to feed the analysis. Ad hoc primary energy factors and greenhouse gas (GHG) emission coefficients are calculated for the selected countries. The costs and the environmental impact for both heating systems are then compared. The outcomes of the study suggest that, in highly fossil fuels dependent electricity mixes, the use of NEHS represents a more efficient decarbonization approach than HP, in spite of its higher efficiency. Additionally, the actual high price of the electric kWh hampers the use of HP in certain cases.
Olaia Eguiarte; Antonio Garrido-Marijuán; Pablo De Agustín-Camacho; Luis Del Portillo; Ander Romero-Amorrortu. Energy, Environmental and Economic Analysis of Air-to-Air Heat Pumps as an Alternative to Heating Electrification in Europe. Energies 2020, 13, 3939 .
AMA StyleOlaia Eguiarte, Antonio Garrido-Marijuán, Pablo De Agustín-Camacho, Luis Del Portillo, Ander Romero-Amorrortu. Energy, Environmental and Economic Analysis of Air-to-Air Heat Pumps as an Alternative to Heating Electrification in Europe. Energies. 2020; 13 (15):3939.
Chicago/Turabian StyleOlaia Eguiarte; Antonio Garrido-Marijuán; Pablo De Agustín-Camacho; Luis Del Portillo; Ander Romero-Amorrortu. 2020. "Energy, Environmental and Economic Analysis of Air-to-Air Heat Pumps as an Alternative to Heating Electrification in Europe." Energies 13, no. 15: 3939.
Nobel energy retrofitting strategies for social housing buildings are presented in this paper, which attempt to reduce their energy demand and enable a low carbon footprint consumption. Both passive and active approaches are applied to a building archetype under 7 representative climates and constructive properties in Europe. On the one hand, simulation results show that passive renovation of buildings through high efficiency envelopes and decentralized ventilation systems would lead to savings from the 69% to the 87% of the required heating demand. Additionally, the cooling demand is reduced up to 32%, although it increases slightly in cold climates due to the addition of insulation. On the other hand, the active trigeneration system includes parabolic trough solar collectors, ORC based cogeneration, adsorption chiller, and a biomass boiler as backup, which reaches a maximum efficiency of 59% in the best scenario. Results show that, a join exploitation of proposed strategies, would mean primary energy savings up to 38% in the worst case, and 61% in the best one. Regarding CO2 emissions, up to 85% reductions are achieved in the seven considered European climates. Finally, analyzing the costs, it is shown that even in the worst case, the energy bills would be reduced in 62%. These results reveal that, the strategies proposed in this paper, depict a feasible way to achieve nZEB while, at the same time, tackling the energy poverty problem that residents in social housing suffer along Europe.
Olaia Eguiarte; Antonio Garrido Marijuan; Pablo De Agustin Camacho; John Fred Velez Jaramillo; Javier Antolín; Luis Alfonso Del Portillo Valdés. STRATEGIES FOR THE ENERGY RETROFITTING OF SOCIAL HOUSING THROUGHOUT EUROPE. DYNA 2019, 94, 605 -609.
AMA StyleOlaia Eguiarte, Antonio Garrido Marijuan, Pablo De Agustin Camacho, John Fred Velez Jaramillo, Javier Antolín, Luis Alfonso Del Portillo Valdés. STRATEGIES FOR THE ENERGY RETROFITTING OF SOCIAL HOUSING THROUGHOUT EUROPE. DYNA. 2019; 94 (1):605-609.
Chicago/Turabian StyleOlaia Eguiarte; Antonio Garrido Marijuan; Pablo De Agustin Camacho; John Fred Velez Jaramillo; Javier Antolín; Luis Alfonso Del Portillo Valdés. 2019. "STRATEGIES FOR THE ENERGY RETROFITTING OF SOCIAL HOUSING THROUGHOUT EUROPE." DYNA 94, no. 1: 605-609.
Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.
Georgios D. Kontes; Georgios I. Giannakis; Víctor Sánchez; Pablo De Agustin-Camacho; Ander Romero; Natalia Panagiotidou; Dimitrios V. Rovas; Simone Steiger; Christopher Mutschler; Gunnar Gruen. Simulation-Based Evaluation and Optimization of Control Strategies in Buildings. Energies 2018, 11, 3376 .
AMA StyleGeorgios D. Kontes, Georgios I. Giannakis, Víctor Sánchez, Pablo De Agustin-Camacho, Ander Romero, Natalia Panagiotidou, Dimitrios V. Rovas, Simone Steiger, Christopher Mutschler, Gunnar Gruen. Simulation-Based Evaluation and Optimization of Control Strategies in Buildings. Energies. 2018; 11 (12):3376.
Chicago/Turabian StyleGeorgios D. Kontes; Georgios I. Giannakis; Víctor Sánchez; Pablo De Agustin-Camacho; Ander Romero; Natalia Panagiotidou; Dimitrios V. Rovas; Simone Steiger; Christopher Mutschler; Gunnar Gruen. 2018. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings." Energies 11, no. 12: 3376.
M. Izquierdo; Pablo De Agustin Camacho. Solar heating by radiant floor: Experimental results and emission reduction obtained with a micro photovoltaic–heat pump system. Applied Energy 2015, 147, 297 -307.
AMA StyleM. Izquierdo, Pablo De Agustin Camacho. Solar heating by radiant floor: Experimental results and emission reduction obtained with a micro photovoltaic–heat pump system. Applied Energy. 2015; 147 ():297-307.
Chicago/Turabian StyleM. Izquierdo; Pablo De Agustin Camacho. 2015. "Solar heating by radiant floor: Experimental results and emission reduction obtained with a micro photovoltaic–heat pump system." Applied Energy 147, no. : 297-307.