In this paper a method is substantiated for predicting the daily electricity consumption level for each day of a whole year, taking into account the seasonal factor, based on only twelve actual electricity consumption data by the months of the year. A mathematical model is described for monitoring and controlling the level of electricity consumption on a daily basis. The model is consistent with a common model for the length of daylight (in hours), which tells us that electricity consumption (at least for general building needs) is determined by the daylight hours. In addition, on the basis of this model, a method for monitoring and diagnostics of electricity consumption is presented, which will enable users to monitor the level of power consumption and be timely notified of any deviations from the theoretical level. Finally, this method gives rise to the operational principle for a proposed device–a smart energy meter–for detecting suspicious deviations from the theoretical level. The device will help timely detect over-consumption (or under-consumption) of electricity in order to take preventive measures. The proposed method consists of the following steps: (1) choice of a function to adequately model the level of electricity consumption (theoretical calculated level), (2) choice of a tubular control neighborhood of the graph of the model function, (3) choice of a criterion on when the smart energy meter should notify the user of an unexpected deviation from the theoretical level in the case of exit from the tubular control neighborhood.
time series, energy consumption monitoring, mathematical modeling, normal distribution law
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