Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock

Abstract

Sound estimates of future heat and electricity demand with high temporal and spatial resolution are needed for energy system planning, grid design, and evaluating demand-side management options and polices on regional and national levels. In this study, smart meter data on electricity consumption in buildings are combined with cross-sectional building information to model hourly electricity consumption within the household and service sectors on a regional basis in Norway. The same modeling approach is applied to model aggregate hourly district heat consumption in three different consumer groups located in Oslo. A comparison of modeled and metered hourly energy consumption shows that hourly variations and aggregate consumption per county and year are reproduced well by the models. However, for some smaller regions, modeled annual electricity consumption is over-or underestimated by more than 20%. Our results indicate that the presented method is useful for modeling the current and future hourly energy consumption of a regional building stock, but that larger and more detailed training datasets are required to improve the models, and more detailed building stock statistics on regional level are needed to generate useful estimates on aggregate regional energy consumption.

Benchmarking Energy Use at University of Almeria (Spain)

“…The second explanatory variable is the weather explanatory variable, defined as the sum of the heating and cooling degree-days during one academic year; its unit is in degrees Celsius. Heating and cooling degree-days (HDD and CDD) are defined as the differences between the average daily outdoor temperature T(o/d) and corresponding base temperatures Tb [26]. The base temperature for heating and cooling is different from place to place.…”

“…Heating and cooling degree-days (HDD and CDD) are defined as the differences between the average daily outdoor temperature T(o/d) and corresponding base temperatures Tb [26]. The base temperature for heating and cooling is different from place to place.…”

“…Twenty-five buildings from our portfolio have an energy intensity inferior than the M.U, and 19 out of those 25 buildings are three time less intense than the k-12 schools in hot and humid zones [9]. In addition, the scatter plot demonstrates that four buildings (30,29,13,26) have an EC greater than 7.0 × 10 5 kWh. These facilities belong to the research and library category.…”

Synthesising Residential Electricity Load Profiles at the City Level Using a Weighted Proportion (Wepro) Model

“…The load profiles outputs are presented as high-resolution data. Existing energy studies were generating 60-min output data [6,21,22,[24][25][26][27][28][29] and one-minute resolution data [14,[16][17][18][19][20]30]. Some works [14,15] have provided a more detailed output in one-minute resolution at once generated 60-min report data.…”

“…A range of statistical analysis methods have also been applied in order to model residential electricity load profiles [6,28,[37][38][39], including determination of the key drivers of residential peak electricity demand. Some studies provided panel datasets including data from smart-meters [24,26,40]. A model was developed using Australian data for the greater Sydney region to analyse and model residential peak demand by providing both daily and seasonal patterns [37].…”