[
1. Sauhats, A., Zemite, L., Petrichenko, L., Moshkin, I., & Jasevics, A. (2018). Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices. Energies, 11 (11). doi:10.3390/en1111322210.3390/en11113222
]Search in Google Scholar
[
2. The International Energy Agency. (2020). National Survey Report of PV Power Applications in Australia 2019. Available at https://iea-pvps.org/wp-content/uploads/2020/09/NSR_Australia-2019.pdf
]Search in Google Scholar
[
3. Simshauser, P. (2016) Distribution Network Prices and Solar PV: Resolving Rate Instability and Wealth Transfers through Demand Tariffs. Energy Economics, 54, 108–122.10.1016/j.eneco.2015.11.011
]Search in Google Scholar
[
4. Parrish, B., Heptonstall, P., Gross, R., & Sovacool, B. K. (2020). A Systematic Review of Motivations, Enablers and Barriers for Consumer Engagement with Residential Demand Response. Energy Policy, 138, 111221.10.1016/j.enpol.2019.111221
]Search in Google Scholar
[
5. Aurora. (2018). Power Sector Modelling: System Cost Impact of Renewables. Report for the National Infrastructure Commission. Oxford: Aurora Energy Research Limited.
]Search in Google Scholar
[
6. Vivid Economics and Imperial College London. (2019). Accelerated Electrification and the GB Electricity System. Report Prepared for Committee on Climate Change. London: Committee on Climate Change.
]Search in Google Scholar
[
7. COWI. (2016). Impact Assessment Study on Downstream Flexibility, Price Flexibility, Demand Response and Smart Metering. Brussels: European Commission DG Energy.
]Search in Google Scholar
[
8. Grunewald, P., & Diakonova, M. (2018). Flexibility, Dynamism and Diversity in Energy Supply and Demand: A Critical Review. Energy Res. Soc.l Sci., 38, 58–66.10.1016/j.erss.2018.01.014
]Search in Google Scholar
[
9. Srivastava, A., Van Passel, S., & Laes, E. (2018). Assessing the Success of Electricity Demand Response Programs: A Meta-Analysis. Energy Res. Soc.l Sci., 40, 110–117.10.1016/j.erss.2017.12.005
]Search in Google Scholar
[
10. Directive (EU) 2019/944 of the European Parliament and of the Council of 5 June 2019 on common rules for the internal market for electricity and amending Directive 2012/27/EU (Text with EEA relevance.) PE/10/2019/REV/1 OJ L 158, 14.6.2019, p. 125–199.
]Search in Google Scholar
[
11. Council of European Energy Regulators. (2019). Monitoring Report on the Performance of European Retail Markets in 2018. CEER Report. Available at https://www.ceer.eu/documents/104400/-/-/5c492f87-c88f-6c78-5852-43f1f13c89e4
]Search in Google Scholar
[
12. Public Utilities Commission of Latvia. (2020). Energy Market Indicators 2020 Q2. Available at https://infogram.com/el_2020-q2-1hnq41vl3gjp43z?live
]Search in Google Scholar
[
13. Elektrum. (2021). Calculator of Electricity Products. Available at https://www.elektrum.lv/en/for-home/for-customers/products/
]Search in Google Scholar
[
14. Alexela. (2021). Laiks mainīt elektrības piegādātāju. Available at https://www.alexela.lv/lv/pakalpojumi-un-cenas/elektribas-tarifiprivatpersonam
]Search in Google Scholar
[
15. Nord Pool. (2021). Historical Market Data. Available at https://www.nordpoolgroup.com/historical-market-data/
]Search in Google Scholar
[
16. EPRI. (2011). The Effect on Electricity Consumption of the Commonwealth Edison Customer Applications Program: Phase 2 Final Analysis. Palo Alto, CA: Electric Power Research Institute.
]Search in Google Scholar
[
17. AECOM. (2011). Energy Demand Research Project: Final Analysis. Hertfordshire: AECOM for Ofgem.
]Search in Google Scholar
[
18. Allcott, H., (2011). Rethinking Real-Time Electricity Pricing. Spec. Sect. : Sustain. Res. Use Econ. Dyn., 33 (4), 820–842.10.1016/j.reseneeco.2011.06.003
]Search in Google Scholar
[
19. Dütschke, E., & Paetz, A.-G. (2013). Dynamic Electricity Pricing – Which Programs do Consumers Prefer? Energy Policy, 59, 226–234.10.1016/j.enpol.2013.03.025
]Search in Google Scholar
[
20. Carmichael, R., Schofield, J., Woolf, M., Bilton, M., Ozaki, R. & Strbacet, G. (2014). Residential consumer attitudes to time-varying pricing. London: Imperial College London.
]Search in Google Scholar
[
21. Torstensson, D., & Wallin, F. (2014). Exploring the Perception for Demand Response among Residential Consumers. Energy Procedia, 61, 2797–2800.10.1016/j.egypro.2014.12.318
]Search in Google Scholar
[
22. US DOE. (2014). Experiences from the consumer behaviour studies on engaging customers. Washington, DC: US Department of Energy.
]Search in Google Scholar
[
23. Broka, Z., Kozadajevs, J., Sauhats, A., Finn, D. P., & Turner, W. J. N. (2016). Modelling residential heat demand supplied by a local smart electric thermal storage system. Paper presented at the 2016 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2016, doi:10.1109/RTUCON.2016.7763128 Retrieved from www.scopus.com10.1109/RTUCON.2016.7763128
]Search in Google Scholar
[
24. Hall, N.L., Jeanneret, T.D., & Rai, A. (2016). Cost-Reflective Electricity Pricing: Consumer Preferences and Perceptions. Energy Policy, 95, 62–72.10.1016/j.enpol.2016.04.042
]Search in Google Scholar
[
25. Bird, J. (2015). Developing the smarter grid: The role of domestic and small and medium enterprise customers. Newcastle-upon-Tyne.
]Search in Google Scholar
[
26. Lebosse, C. (2016). Assessment of the social behaviour of the residential customers after on site tests. Grid4EU DEMO6 - dD6.8-1.
]Search in Google Scholar
[
27. Bartusch, C., Odlare, M., Vassileva, I.N., Wallin, F., & Wester, L. (2011). Introducing a Demand-Based Electricity Distribution Tariff in the Residential Sector: Demand Response and Customer Perception. Energy Policy, 39 (9), 5008–5025.10.1016/j.enpol.2011.06.013
]Search in Google Scholar
[
28. Bradley, P., Coke, A., & Leach, M. (2016). Financial Incentive Approaches for Reducing Peak Electricity Demand, Experience from Pilot Trials with a UK Energy Provider. Energy Policy, 98, 108–120.10.1016/j.enpol.2016.07.022
]Search in Google Scholar
[
29. Friis, F., & Haunstrup Christensen, T. (2016). The Challenge of Time Shifting Energy Demand Practices: Insights from Denmark. Energy Res. Soc.l Sci., 19, 124–133.10.1016/j.erss.2016.05.017
]Search in Google Scholar
[
30. Capehart, B.L., Kennedy, W.J., Turner, W.C. (2008). Guide to energy management. (Fifth ed.). Atlanta, US: The Fairmont Press, Inc.
]Search in Google Scholar
[
31. Central Statistical Bureau. (2016). EPM210. Mājokļos izmantotās elektroierīces un elektroierīču vidējais vecums. Available at https://data1.csb.gov.lv/pxweb/lv/vide/vide__energetika__energ_pat/EPM210.px/Lecamwasam, L. (2012). Guide to Best Practice Maintenance & Operation of HVAC Systems for Energy Efficiency. National Strategy on Energy Efficiency. Australia: Department of Climate Change and Energy Efficiency. Available at https://www.airah.org.au/imis15_prod/Content_Files/UsefulDocuments/DCCEE_HVAC_HESS_GuideToBestPractice2012.PDF
]Search in Google Scholar
[
32. Tavakkoli, M., Fattaheian-dehkordi, S., Pourakbari-kasmaei, M., Liski, M., & Lehtonen, M. (2019). An Incentive Based Demand Response by HVAC Systems in Residential Houses. Proceedings of 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) (pp. 1–5), 29 September 2019–2 October 2019, Bucharest, Romania. doi:10.1109/isgteurope.2019.890569110.1109/ISGTEurope.2019.8905691
]Search in Google Scholar
[
33. Petrie, C., Gupta, S., Rao, V., & Nutter, B. (2018). Energy Efficient Control Methods of HVAC Systems for Smart Campus. 2018 IEEE Green Technologies Conference (GreenTech). DOI:10.1109/GREENTECH.2018.00032 Perez, K.X., Baldea, M., & Edgar, T.F. (2016). Integrated Smart Appliance Scheduling and HVAC Control for Peak Residential Load Management. In 2016 American Control Conference (ACC), (pp. 1458–1463), 6–8 July 2016, Boston, Massachusetts, USA.
]Search in Google Scholar
[
34. LVĢMC. (2021). Datu meklēšana. Available at https://www.meteo.lv/meteorologija-datu-meklesana/?nid=461
]Search in Google Scholar
[
35. Petrichenko, R., Baltputnis, K., Sauhats, A., & Sobolevsky, D. (2017). District Heating Demand Short-Term Forecasting. In 17th IEEE International Conference on Environment and Electrical Engineering and 2017 1st IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2017, 6–9 June 2017, Milan, Italy. DOI:10.1109/EEEIC.2017.797763310.1109/EEEIC.2017.7977633
]Search in Google Scholar
