IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v66y2014icp295-304.html
   My bibliography  Save this article

Calculation method for electricity end-use for residential lighting

Author

Listed:
  • Rosenberg, Eva

Abstract

Knowledge of the electricity demand for different electrical appliances in households is very important in the work to reduce electricity use in households. Metering of end-uses is expensive and time consuming and therefore other methods for calculation of end-use electricity can be very useful. This paper presents a method to calculate the electricity used for lighting in households based on regression analysis of daily electricity consumption, out-door temperatures and the length of daylight at the same time and location. The method is illustrated with analyses of 45 Norwegian households. The electricity use for lighting in an average Norwegian household is calculated to 1050 kWh/year or 6% of total electricity use. The results are comparable to metering results of lighting in other studies in the Nordic countries. The methodology can also be used to compensate for the seasonal effect when metering electricity for lighting less than a year. When smart meters are more commonly available, the possible adaption of this method will increase, and the need for end-use demand calculations will still be present.

Suggested Citation

  • Rosenberg, Eva, 2014. "Calculation method for electricity end-use for residential lighting," Energy, Elsevier, vol. 66(C), pages 295-304.
    • Handle: RePEc:eee:energy:v:66:y:2014:i:c:p:295-304
    DOI: 10.1016/j.energy.2013.12.049
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544213011195
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2013.12.049?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Scott Kelly, 2011. "Do homes that are more energy efficient consume less energy?: A structural equation model for England's residential sector," Working Papers EPRG 1117, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    2. Zahedi, Gholamreza & Azizi, Saeed & Bahadori, Alireza & Elkamel, Ali & Wan Alwi, Sharifah R., 2013. "Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada," Energy, Elsevier, vol. 49(C), pages 323-328.
    3. Ma, Linwei & Allwood, Julian M. & Cullen, Jonathan M. & Li, Zheng, 2012. "The use of energy in China: Tracing the flow of energy from primary source to demand drivers," Energy, Elsevier, vol. 40(1), pages 174-188.
    4. Michael Parti & Cynthia Parti, 1980. "The Total and Appliance-Specific Conditional Demand for Electricity in the Household Sector," Bell Journal of Economics, The RAND Corporation, vol. 11(1), pages 309-321, Spring.
    5. Kelly, Scott, 2011. "Do homes that are more energy efficient consume less energy?: A structural equation model of the English residential sector," Energy, Elsevier, vol. 36(9), pages 5610-5620.
    6. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    7. Kaldellis, John & Zafirakis, Dimitrios, 2012. "Experimental investigation of the optimum photovoltaic panels’ tilt angle during the summer period," Energy, Elsevier, vol. 38(1), pages 305-314.
    8. Bladh, Mats & Krantz, Helena, 2008. "Towards a bright future? Household use of electric light: A microlevel study," Energy Policy, Elsevier, vol. 36(9), pages 3521-3530, September.
    9. Rosenquist, Greg & McNeil, Michael & Iyer, Maithili & Meyers, Stephen & McMahon, James, 2006. "Energy efficiency standards for equipment: Additional opportunities in the residential and commercial sectors," Energy Policy, Elsevier, vol. 34(17), pages 3257-3267, November.
    10. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2013. "Evaluation of time series techniques to characterise domestic electricity demand," Energy, Elsevier, vol. 50(C), pages 120-130.
    11. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
    12. Gouveia, João Pedro & Fortes, Patrícia & Seixas, Júlia, 2012. "Projections of energy services demand for residential buildings: Insights from a bottom-up methodology," Energy, Elsevier, vol. 47(1), pages 430-442.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. García-Gusano, Diego & Espegren, Kari & Lind, Arne & Kirkengen, Martin, 2016. "The role of the discount rates in energy systems optimisation models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 56-72.
    2. Emeakaroha, Anthony & Ang, Chee Siang & Yan, Yong & Hopthrow, Tim, 2014. "Integrating persuasive technology with energy delegates for energy conservation and carbon emission reduction in a university campus," Energy, Elsevier, vol. 76(C), pages 357-374.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhu, Mengshu & Huang, Ying & Wang, Si-Nuo & Zheng, Xinye & Wei, Chu, 2023. "Characteristics and patterns of residential energy consumption for space cooling in China: Evidence from appliance-level data," Energy, Elsevier, vol. 265(C).
    2. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    3. Boukarta Soufiane & Berezowska-Azzag Ewa, 2018. "Assessing Households’ Gas and Electricity Consumption: A Case Study of Djelfa, Algeria," Quaestiones Geographicae, Sciendo, vol. 37(4), pages 111-129, December.
    4. Maria Cecilia P Moura & Steven J Smith & David B Belzer, 2015. "120 Years of U.S. Residential Housing Stock and Floor Space," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
    5. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    6. Besagni, Giorgio & Borgarello, Marco & Premoli Vilà, Lidia & Najafi, Behzad & Rinaldi, Fabio, 2020. "MOIRAE – bottom-up MOdel to compute the energy consumption of the Italian REsidential sector: Model design, validation and evaluation of electrification pathways," Energy, Elsevier, vol. 211(C).
    7. Fei Wang & Yili Yu & Xinkang Wang & Hui Ren & Miadreza Shafie-Khah & João P. S. Catalão, 2018. "Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression," Energies, MDPI, vol. 11(5), pages 1-26, May.
    8. Linwei Pan & Minglei Zhu & Ningning Lang & Tengfei Huo, 2020. "What Is the Amount of China’s Building Floor Space from 1996 to 2014?," IJERPH, MDPI, vol. 17(16), pages 1-17, August.
    9. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    10. Chung, Mo & Park, Hwa-Choon, 2015. "Comparison of building energy demand for hotels, hospitals, and offices in Korea," Energy, Elsevier, vol. 92(P3), pages 383-393.
    11. Wang, Xia & Ding, Chao & Cai, Weiguang & Luo, Lizi & Chen, Mingman, 2021. "Identifying household cooling savings potential in the hot summer and cold winter climate zone in China: A stochastic demand frontier approach," Energy, Elsevier, vol. 237(C).
    12. Comodi, Gabriele & Cioccolanti, Luca & Renzi, Massimiliano, 2014. "Modelling the Italian household sector at the municipal scale: Micro-CHP, renewables and energy efficiency," Energy, Elsevier, vol. 68(C), pages 92-103.
    13. Streltsov, Artem & Malof, Jordan M. & Huang, Bohao & Bradbury, Kyle, 2020. "Estimating residential building energy consumption using overhead imagery," Applied Energy, Elsevier, vol. 280(C).
    14. Kelly, Scott & Shipworth, Michelle & Shipworth, David & Gentry, Michael & Wright, Andrew & Pollitt, Michael & Crawford-Brown, Doug & Lomas, Kevin, 2013. "Predicting the diversity of internal temperatures from the English residential sector using panel methods," Applied Energy, Elsevier, vol. 102(C), pages 601-621.
    15. Estiri, Hossein, 2014. "Building and household X-factors and energy consumption at the residential sector," Energy Economics, Elsevier, vol. 43(C), pages 178-184.
    16. Scott Kelly & Michael Pollitt & Doug Crawford-Brown, 2011. "Building performance evaluation and certification in the UK: a critical review of SAP?," Working Papers EPRG 1219, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    17. Estiri, Hossein & Zagheni, Emilio, 2018. "Evaluating the Age-Energy Consumption Profile in Residential Buildings," SocArXiv yqkva, Center for Open Science.
    18. Kelly, Scott & Crawford-Brown, Doug & Pollitt, Michael G., 2012. "Building performance evaluation and certification in the UK: Is SAP fit for purpose?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6861-6878.
    19. Rafael de Arce & Ramón Mahía, 2019. "Drivers of Electricity Poverty in Spanish Dwellings: A Quantile Regression Approach," Energies, MDPI, vol. 12(11), pages 1-18, May.
    20. Guo, Ji & Xu, Yuanjing & Qu, Yao & Wang, Yiting & Wu, Xianhua, 2023. "Exploring factors affecting household energy consumption in the internet era: Empirical evidence from Chinese households," Energy Policy, Elsevier, vol. 183(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:66:y:2014:i:c:p:295-304. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.