Calculation method for electricity end-use for residential lighting

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.

[1]  M. Parti,et al.  The Total and Appliance-Specific Conditional Demand for Electricity in the Household Sector , 1980 .

[2]  E. Georgopoulou,et al.  Models for mid-term electricity demand forecasting incorporating weather influences , 2006 .

[3]  R. Rajagopal,et al.  Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior , 2013 .

[4]  V. Sethi,et al.  Development of dual purpose greenhouse coupled with north wall utilization for higher economic gains , 2011 .

[5]  Barbara Schlomann,et al.  Characterization of the household electricity consumption in the EU, potential energy savings and sp , 2011 .

[6]  Scott Kelly,et al.  Do homes that are more energy efficient consume less energy?: A structural equation model of the English residential sector , 2011 .

[7]  K. H. Tiedemann Using conditional demand analysis to estimate residential energy use and energy savings , 2007 .

[8]  Zheng Li,et al.  The use of energy in China: Tracing the flow of energy from primary source to demand drivers , 2012 .

[9]  Mark Rylatt,et al.  A simple model of domestic lighting demand , 2004 .

[10]  David Infield,et al.  Domestic lighting: A high-resolution energy demand model , 2009 .

[11]  John K. Kaldellis,et al.  Experimental investigation of the optimum photovoltaic panels’ tilt angle during the summer period , 2012 .

[12]  James E. McMahon,et al.  Energy efficiency standards for equipment: Additional opportunities in the residential and commercial sectors , 2006 .

[13]  Ewa Wäckelgård,et al.  A combined Markov-chain and bottom-up approach to modelling of domestic lighting demand , 2009 .

[14]  Helge V. Larsen,et al.  Long-term forecasting of hourly electricity load: Identification of consumption profiles and segmentation of customers , 2013 .

[15]  Aidan Duffy,et al.  Evaluation of time series techniques to characterise domestic electricity demand , 2013 .

[16]  J. Seixas,et al.  Projections of energy services demand for residential buildings: Insights from a bottom-up methodology , 2012 .

[17]  Runsheng Tang,et al.  Optical performance and design optimization of V-trough concentrators for photovoltaic applications , 2011 .

[18]  Mats Bladh,et al.  Towards a bright future? Household use of electric light: A microlevel study , 2008 .

[19]  A. Elkamel,et al.  Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada , 2013 .