scholarly journals Current heating energy demand by the residential sector in city Prishtina based on the main resources

2017 ◽  
Vol 12 (1) ◽  
pp. 147-158 ◽  
Author(s):  
Petrit Ahmeti ◽  
Ilir Dalipi ◽  
Agon Basha ◽  
István Kistelegdi
Keyword(s):  
Energy Demand ◽  
Residential Sector ◽  
Heating Energy Demand

scholarly journals Heating and Cooling Degree-Days Climate Change Projections for Portugal

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 715
Author(s):  
Cristina Andrade ◽  
Sandra Mourato ◽  
João Ramos
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Thermal Comfort ◽  
Energy Demand ◽  
Residential Buildings ◽  
Degree Days ◽  
Climate Change Projections ◽  
Heating And Cooling ◽  
Cooling Degree Days ◽  
Heating Energy Demand

Climate change is expected to influence cooling and heating energy demand of residential buildings and affect overall thermal comfort. Towards this end, the heating (HDD) and cooling (CDD) degree-days along with HDD + CDD were computed from an ensemble of seven high-resolution bias-corrected simulations attained from EURO-CORDEX under two Representative Concentration Pathways (RCP4.5 and RCP8.5). These three indicators were analyzed for 1971–2000 (from E-OBS) and 2011–2040, and 2041–2070, under both RCPs. Results predict a decrease in HDDs most significant under RCP8.5. Conversely, it is projected an increase of CDD values for both scenarios. The decrease in HDDs is projected to be higher than the increase in CDDs hinting to an increase in the energy demand to cool internal environments in Portugal. Statistically significant linear CDD trends were only found for 2041–2070 under RCP4.5. Towards 2070, higher(lower) CDD (HDD and HDD + CDD) anomaly amplitudes are depicted, mainly under RCP8.5. Within the five NUTS II

Characteristics of indoor temperatures over winter for Belgrade urban dwellings: Indications of thermal comfort and space heating energy demand

2012 ◽  
Vol 47 ◽  
pp. 506-514 ◽  
Author(s):  
M. Kavgic ◽  
A. Summerfield ◽  
D. Mumovic ◽  
Z.M. Stevanovic ◽  
V. Turanjanin ◽  
...  
Keyword(s):  
Thermal Comfort ◽  
Energy Demand ◽  
Space Heating ◽  
Heating Energy Demand

scholarly journals An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids

2018 ◽  
Vol 30 (1) ◽  
pp. 63-80 ◽  
Author(s):  
Paraskevas Panagiotidis ◽  
Andrew Effraimis ◽  
George A Xydis
Keyword(s):  
Demand Response ◽  
Energy Demand ◽  
Moving Average ◽  
Electricity Consumption ◽  
End Users ◽  
Market Model ◽  
Residential Sector ◽  
Response Strategies ◽  
System Operator ◽  
Modelling Methodology

The main aim of this work is to reduce electricity consumption for consumers with an emphasis on the residential sector in periods of increased demand. Efforts are focused on creating a methodology in order to statistically analyse energy demand data and come up with forecasting methodology/pattern that will allow end-users to organize their consumption. This research presents an evaluation of potential Demand Response programmes in Greek households, in a real-time pricing market model through the use of a forecasting methodology. Long-term Demand Side Management programs or Demand Response strategies allow end-users to control their consumption based on the bidirectional communication with the system operator, improving not only the efficiency of the system but more importantly, the residential sector-associated costs from the end-users’ side. The demand load data were analysed and categorised in order to form profiles and better understand the consumption patterns. Different methods were tested in order to come up with the optimal result. The Auto Regressive Integrated Moving Average modelling methodology was selected in order to ensure forecasts production on load demand with the maximum accuracy.

An Energy-Related CityGML ADE and Its Application for Heating Demand Calculation

2019 ◽  
pp. 1306-1323
Author(s):  
Marcel Bruse ◽  
Romain Nouvel ◽  
Parag Wate ◽  
Volker Kraut ◽  
Volker Coors
Keyword(s):  
Energy Demand ◽  
Building Energy ◽  
Energy Simulation ◽  
Data Sets ◽  
Application Domain ◽  
Data Formats ◽  
Building Energy Systems ◽  
Domain Extension ◽  
Energy Simulations ◽  
Heating Energy Demand

Different associated properties of city models like building geometries, building energy systems, building end uses, and building occupant behavior are usually saved in different data formats and are obtained from different data sources. Experience has shown that the integration of these data sets for the purpose of energy simulation on city scale is often cumbersome and error prone. A new application domain extension for CityGML has been developed in order to integrate energy-related figures of buildings, thermal volumes, and facades with their geometric descriptions. These energy-related figures can be parameters or results of energy simulations. The applicability of the new application domain extension has been demonstrated for heating energy demand calculation.

scholarly journals Central heating settings and heating energy demand in low energy social housing in the United Kingdom

2019 ◽  
Vol 158 ◽  
pp. 3658-3663
Author(s):  
Adorkor Bruce-Konuah ◽  
Rory V. Jones ◽  
Alba Fuertes ◽  
Pieter de Wilde
Keyword(s):  
United Kingdom ◽  
Social Housing ◽  
Energy Demand ◽  
Low Energy ◽  
The United Kingdom ◽  
Central Heating ◽  
Heating Energy Demand

scholarly journals Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1158
Author(s):  
Behrad Bezyan ◽  
Radu Zmeureanu
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Energy Use ◽  
Machine Learning Techniques ◽  
Northern Canada ◽  
Automation And Control ◽  
And Performance ◽  
Energy Signature ◽  
Heating Energy Demand ◽  
And Control

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications.

scholarly journals Energy Demand Model for Residential Sector: A First Principles Approach

2017 ◽  
Vol 8 (3) ◽  
pp. 1215-1224 ◽  
Author(s):  
Rajesh Subbiah ◽  
Anamitra Pal ◽  
Eric K. Nordberg ◽  
Achla Marathe ◽  
Madhav V. Marathe
Keyword(s):  
First Principles ◽  
Energy Demand ◽  
Demand Model ◽  
Residential Sector

scholarly journals Geothermal source heat pump performance for a greenhouse heating system: an experimental study

2016 ◽  
Vol 47 (3) ◽  
pp. 164-170 ◽  
Author(s):  
Alexandros Sotirios Anifantis ◽  
Simone Pascuzzi ◽  
Giacomo Scarascia-Mugnozza
Keyword(s):  
Experimental Data ◽  
Heat Pump ◽  
Energy Demand ◽  
Thermal Conditions ◽  
Heating System ◽  
Coefficient Of Performance ◽  
Working Fluid ◽  
Geothermal Heat ◽  
Pump Performance ◽  
Heating Energy Demand

Greenhouses play a significant function in the modern agriculture economy even if require great amount of energy for heating systems. An interesting solution to alleviate the energy costs and environmental problems may be represented by the use of geothermal energy. The aim of this paper, based on measured experimental data, such as the inside greenhouse temperature and the heat pump performance (input and output temperatures of the working fluid, electric consumption), was the evaluation of the suitability of low enthalpy geothermal heat sources for agricultural needs such as greenhouses heating. The study was carried out at the experimental farm of the University of Bari, where a greenhouse was arranged with a heating system connected to a ground-source heat pump (GSHP), which had to cover the thermal energy request. The experimental results of this survey highlight the capability of the geothermal heat source to ensue thermal conditions suitable for cultivation in greenhouses even if the compressor inside the heat pump have operated continuously in a fluctuating state without ever reaching the steady condition. Probably, to increase the performance of the heat pump and then its coefficient of performance within GSHP systems for heating greenhouses, it is important to analyse and maximise the power conductivity of the greenhouse heating system, before to design an expensive borehole ground exchanger. Nevertheless, according to the experimental data obtained, the GSHP systems are effective, efficient and environmental friendly and may be useful to supply the heating energy demand of greenhouses.

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