Modeling and Simulation of Household Appliances Power Consumption

Abstract

The consumption of household appliances tends to increase. Therefore, the application of energy efficiency measurements is urgently needed to reduce the levels of power consumption. Over the last years, various methods have been used to predict household electricity consumption. As a novelty, this paper proposed a method of predicting the consumption of household appliances by evaluating statistical distributions (Kolmogorov–Smirnov Test and Pearson’s X2 test). To test the veracity of the evaluations, first, a set of random values was simulated for each hour, and their respective averages were calculated. These were compared with the averages of the real values for each hour. With the exception of HVAC during working days, great results were obtained. For the refrigerator, the maximum error was 3.91%, while for the lighting, it was 4.27%. At the point of consumption, the accuracy was even higher, with an error of 1.17% for the dryer while for the washing machine and dishwasher, their minimum errors were less than 1%. The error results confirm that the applied methodology is perfectly acceptable for modeling household appliance consumption and consequently predicting it. However, these consumptions can be only extrapolated to dwellings with similar surface areas and habitats.

Smart System for Reducing Standby Energy Consumption in Residential Appliances

“…This application enables users to set up and manage the system. It features two buttons for toggling the system on and off (1,2), displays indicating the system s active status and the power consumption of connected appliances (3,4), and two buttons for ad justing the time and current settings (5,6). Additionally, there are two input fields for spec ifying the delay time and current threshold required for system activation (7,6).…”

“…Several articles have suggested methods to decrease power consumption in households, focusing on predicting the energy usage of appliances through statistical distributions, machine learning, binary gray wolf optimization, and modeling on-off times. However, these studies do not explicitly demonstrate whether these approaches effectively reduce standby consumption [6][7][8]. In the context of reducing power consumption in residential buildings, solutions involving smart energy management systems have been proposed.…”

Système Intelligent Pour La Réduction De La Consommation Électrique en Veille Des Équipements Ménagers

“…In past articles, one of the solutions for the power consumption reduction in a household was to propose methods of predicting the power consumption of household appliances by using statistical distributions, machine learning, binary grey wolf optimization, and modeling the on-off times. Still, the studies do not clearly show if these factors are responsible for reducing stand-by consumption [10][11][12]. Regarding the power consumption reduction in residential buildings, the solution for smart energy management systems was proposed, which integrates the system in a smart building/house where the main purpose is to optimize the power consumption by turning off/on the appliances without affecting the user's comfort [13,14].…”

Prediction of a Grid-Connected Photovoltaic Park’s Output with Artificial Neural Networks Trained by Actual Performance Data

“…Knowledge of the building's consumption profile is a necessary starting point for the improvement of the building's energy management and application of energy-saving actions. Villanueva et al proposed a method for predicting the consumption of household appliances by evaluating statistical distributions (Kolmogorov-Smirnov and Pearson's X 2 tests) [22]. Kalogirou et al proposed a model for the prediction of energy consumption in a passive solar building in order to generate a mapping between the above easily measurable inputs and the desired output, i.e., the building's energy consumption [23].…”