ORIGINAL ARTICLE
Optimization of Municipal Energy Systems with the Use of an Intelligent Analytical System
1
University of Zielona Góra, Faculty of Civil Engineering, Architecture and Environmental Engineering
2
Wrocław University of Science and Technology, Faculty of Environmental Engineering
Online publication date: 2019-01-03
Publication date: 2018-09-01
Civil and Environmental Engineering Reports 2018;28(3):132-144
KEYWORDS
ABSTRACT
The paper presents the analytical and consultancy system which aims at a complex, comprehensive, multi-criteria energy performance analysis of a given building or a group of buildings and at making a recommendation for an energy source with regard to CO2 emission and investment costs determined on the basis of indicators included in the knowledge databases. The analytical and consultancy system employs advanced energy performance computer simulations of buildings as well as innovative analytical algorithms worked out and contributed by the authors, including those based on the knowledge base developed on the grounds of performance data from selected buildings of various types situated in a dozen or so cities of different population in Poland.
REFERENCES (39)
1.
Bačeković I., Østergaard P. A., A smart energy system approach vs a nonintegrated renewable energy system approach to designing a future energy system in Zagreb, Energy, vol. 155, pp. 824–837, Jul. 2018.
2.
Yazdanie M., Densing M., Wokaun A., Cost optimal urban energy systems planning in the context of national energy policies: A case study for the city of Basel, Energy Policy, vol. 110, pp. 176–190, Nov. 2017.
3.
Nilsson J. S., Mårtensson A., Municipal energy-planning and development of local energy-systems, Appl. Energy, vol. 76, no. 1–3, pp. 179–187, Sep. 2003.
4.
Regulski B., Ziembicki P., Bernasiński J., Węglarz A., Rynek ciepł owniczy w Polsce, Rynek Energii, vol. 113, no. 4, pp. 9–16, 2014.
5.
Wojdyga K., Chorzelski M., Chances for Polish district heating systems, Energy Procedia, vol. 116, pp. 106–118, Jun. 2017.
6.
Hast A., Syri S., Lekavičius V., Galinis A., District heating in cities as a part of low-carbon energy system, Energy, vol. 152, pp. 627–639, Jun. 2018.
7.
Paiho S., Reda F., Towards next generation district heating in Finland, Renew. Sustain. Energy Rev., vol. 65, pp. 915–924, Nov. 2016.
8.
Balić D., Maljković D., Lončar D., Multi-criteria analysis of district heating system operation strategy, Energy Convers. Manag., vol. 144, pp. 414–428, Jul. 2017.
9.
Dominković D. F., Wahlroos M., Syri S., Pedersen A. S., Influence of different technologies on dynamic pricing in district heating systems: Comparative case studies, Energy, vol. 153, pp. 136–148, Jun. 2018.
10.
Morvaj B., Evins R., Carmeliet J., Optimising urban energy systems: Simultaneous system sizing, operation and district heating network layout, Energy, vol. 116, pp. 619–636, Dec. 2016.
11.
Ziemele J., Cilinskis E., Blumberga D., Pathway and restriction in district heating systems development towards 4th generation district heating, Energy, vol. 152, pp. 108–118, Jun. 2018.
12.
Gao L. et al., Technologies in Smart District Heating System, Energy Procedia, vol. 142, pp. 1829–1834, Dec. 2017.
13.
Good N., Martínez Ceseña E. A., Mancarella P., Ten questions concerning smart districts, Build. Environ., vol. 118, pp. 362–376, Jun. 2017.
14.
Yabanova I., Keçebas A., Development of ANN model for geothermal district heating system and a novel PID-based control strategy, Appl. Therm. Eng., vol. 51, no. 1–2, pp. 908–916, Mar. 2013.
15.
Lichtenegger K., Wöss D., Halmdienst C., Höftberger E., Schmidl C., Pröll T., Intelligent heat networks: First results of an energy-information-costmodel, Sustain. Energy, Grids Networks, vol. 11, pp. 1–12, Sep. 2017.
16.
AlFaris F., Juaidi A., Manzano-Agugliaro F., Intelligent homes’ technologies to optimize the energy performance for the net zero energy home, Energy Build., vol. 153, pp. 262–274, Oct. 2017.
17.
Amber K. P., Ahmad R., Aslam M. W., Kousar A., Usman M., Khan M. S., Intelligent Techniques for Forecasting Electricity Consumption of Buildings, Energy, vol. 157, pp. 886–893, Aug. 2018.
18.
Ziembicki P., Kozioł J., Mendecka B., Innowacyjne metody zarządzania w energetyce komunalnej. Zielona Góra, Oficyna Wydawnicza Uniwersytetu Zielonogórskiego, 2018.
19.
Calvillo C. F., Sánchez-Miralles A., Villar J., Energy management and planning in smart cities, Renew. Sustain. Energy Rev., vol. 55, pp. 273–287, Mar. 2016.
20.
Mosannenzadeh F., Bisello A., Vaccaro R., D’Alonzo V., Hunter G. W., Vettorato D., Smart energy city development: A story told by urban planners, Cities, vol. 64, pp. 54–65, Apr. 2017.
21.
Mosannenzadeh F., Di Nucci M. R., Vettorato D., Identifying and prioritizing barriers to implementation of smart energy city projects in Europe: An empirical approach, Energy Policy, vol. 105, pp. 191–201, Jun. 2017.
22.
Rismanchi B., District energy network (DEN), current global status and future development, Renew. Sustain. Energy Rev., vol. 75, pp. 571–579, Aug. 2017.
23.
Aghamolaei R., Shamsi M. H., Tahsildoost M., O’donnell J., Review of district-scale energy performance analysis: Outlooks towards holistic urban frameworks, Sustain. Cities Soc., vol. 41, pp. 252–264, Aug. 2018.
24.
Colmenar-Santos A., Rosales-Asensio E., Borge-Diez D., Blanes-Peiró J. J., District heating and cogeneration in the EU-28: Current situation, potential and proposed energy strategy for its generalisation, Renew. Sustain. Energy Rev., vol. 62, pp. 621–639, Sep. 2016.
25.
Ahn J., Chung D. H., Cho S., Energy cost analysis of an intelligent building network adopting heat trading concept in a district heating model, Energy, vol. 151, pp. 11–25, May 2018.
26.
Wang N. et al., Hydraulic resistance identification and optimal pressure control of district heating network, Energy Build., vol. 170, pp. 83–94, Jul. 2018.
27.
Schmidt D. et al., Low Temperature District Heating for Future Energy Systems, Energy Procedia, vol. 116, pp. 26–38, Jun. 2017.
28.
Vandermeulen A., van der Heijde B., Helsen L., Controlling district heating and cooling networks to unlock flexibility: A review, Energy, vol. 151, pp. 103–115, May 2018.
29.
Johansson C., Bergkvist M., Geysen D., De Somer O., Lavesson N., Vanhoudt D., Operational Demand Forecasting in District Heating Systems Using Ensembles of Online Machine Learning Algorithms, Energy Procedia, vol. 116, pp. 208–216, Jun. 2017.
30.
Li H., Wang S. J., Load Management in District Heating Operation, Energy Procedia, vol. 75, pp. 1202–1207, Aug. 2015.
31.
Pizzolato A., Sciacovelli A., Verda V., Centralized control of district heating networks during failure events using discrete adjoint sensitivities, Energy, Sep. 2017.
32.
Raatikainen M., Skön J. P., Leiviskä K., Kolehmainen M., Intelligent analysis of energy consumption in school buildings, Appl. Energy, vol. 165, pp. 416–429, Mar. 2016.
33.
Petković D., Protić M., Shamshirband S., Akib S., Raos M., Marković D., Evaluation of the most influential parameters of heat load in district heating systems, Energy Build., vol. 104, pp. 264–274, Oct. 2015.
34.
Izadyar N., Ong H. C., Shamshirband S., Ghadamian H., Tong C. W., Intelligent forecasting of residential heating demand for the District Heating System based on the monthly overall natural gas consumption, Energy Build., vol. 104, pp. 208–214, Oct. 2015.
35.
Shamshirband S. et al., Heat load prediction in district heating systems with adaptive neuro-fuzzy method, Renew. Sustain. Energy Rev., vol. 48, pp. 760–767, Aug. 2015.
36.
Ahn J. Cho S., Development of an intelligent building controller to mitigate indoor thermal dissatisfaction and peak energy demands in a district heating system, Build. Environ., vol. 124, pp. 57–68, Nov. 2017.
37.
R Core Team, R: A Language and Environment for Statistical Computing. Vienna, 2017.
38.
US Department of Energy, ‘EnergyPlus Engineering Reference: The Reference to EnergyPlus Calculations’, 2010.
39.
Klimczak M., Bojarski J., Ziembicki P., Kȩskiewicz P., Analysis of the impact of simulation model simplifications on the quality of low-energy buildings simulation results, Energy Build., vol. 169, pp. 141–147, Jun. 2018.
Share
RELATED ARTICLE
| eISSN: | 2450-8594 |
| ISSN: | 2080-5187 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
