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Report RSE 17002204

Electricity consumption and role of the temperature variable

This report illustrates the preliminary activity carried out in order to assess the typical response of the electrical consumption of some household appliances to the change in atmospheric temperature and to realize simple algorithms that allow to predict the electrical demand, in particular the actual load, for each zone of the electric market.

This report describes the preliminary research activity on two topics of particular interest to the electrical sector. The first activity is aimed at assessing the influence of weather variability on end users' electrical consumption and in particular on the use of some household appliances. This activity is based on the electrical consumption measures currently being carried out by RSE, performed for a few months in some homes located in the city of Milan and in the province of Varese. In order to understand how any variations in the consumption of electricity monitored during the experiment may be induced and/or forced by the change of weather conditions, the available data sets were analysed with the aim of highlighting possible correlations between electrical consumption data and air temperature. This activity may be useful to the end user in order to contain the costs by appropriate use of temperature-sensitive household appliances and may also serve to educate citizens more aware and more sensitive to environmental issues. The result of this first analysis has shown how some end uses are particularly sensitive to changes in atmospheric temperatures but, in order to improve the study so far, it would be necessary, in addition to increasing the number of data and dwellings monitored, to obtain more information on how and by whom the house is inhabited.

The second activity, on the other hand, has the prime objective of laying the foundations for a study devoted to the forecast of electricity demand on different spatial and temporal scales. Therefore, the databases representing the demand for electricity at different spatial resolutions have been sought and acquired. In particular, the actual load data of all the electricity market areas was acquired at a hourly temporal resolution (2015 and 2016). Regarding the meteorological and socioeconomic data, the dataset utilised was based, respectively, on the output of a limited area model used at RSE, in particular the temperature data produced by the RAMS model (year 2015 and 2016) were extracted and partly used, and on a socioeconomic variable called calendar variable that associates distinct weights to every day of the week, considering in particular also the various holidays present during the year. The first algorithms made are mainly based on the autoregressive component of the actual load variable by including the calendar variable in some.

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