# Household Energy Management
The objective function takes into account the expected electricity costs for the next day (RTP with Inclining Block Rate) and satisfaction with the comfort of using energy by the consumer based on historical data defining the trends in the work of household consumers. Another DSM&R optimization model based on heuristic evolutionary algorithm is presented in [20]. It presents the results of simulation tests for various loads in three areas of services with final consumers. In addition to the algorithms belonging to the two mentioned groups: classical and heuristic, there are other solutions known in the literature, presented below. In micro-networks containing energy storage, DSM&R solution was proposed based on modelling of state space [16]. This model integrates the structural, time and logical features of micro-networks to identify and build a wide variety of energy management strategies. The Demand Response Optimization (DROP) algorithm was used to make optimal decisions for DSM&R activities in plants equipped with distributed energy resources (DER) [21]. Issues concerning the use of DSM&R in industrial plants are presented in [22]. Optimal models of industrial load scheduling in SG environment were determined and methods of solving optimization models were discussed. An example of using game theory in DSM&R to smooth the ratio of the peak value to the average power value while rewarding customers for an accurate load forecast is presented in [17]. In [23] a model is presented taking into account hourly profiles of electricity consumption for various household appliances and their share in annual consumption. The result of this research was to offer households economic and/or ecological incentives to change consumption at specific times. Similar activities, but in relation to asmall group of households on a small island are presented in [24]. The correct operation of DSM&R at national level depends on its implementation at the final consumer level [25]. In most cases, DSM&R optimization was used for households. The use of the Internet of Things (IoT) [15] is a favorable step towards introducing DSM&R in households. In this approach two classifications are distinguished: Energy-efficient Solutions and Energy Harvesting [26]. A characteristic feature of this solution is collecting a large data set from the distributed system that requires processing. To this end, the approaches presented in [27; 28] can be used. The selected publications presented above confirm the need for work in the field of DSM&R for final consumers. The largest final consumers of energy are industrial consumers, followed by households. The application of DSM&R to industrial consumers may be more complex compared to households. This is related to reliability management, which is definitely more important for industrial consumers [29]. In [19] it was pointed out that from the group of final consumers analyzed, households respond more in DSM&R programs. This requires work to develop further DSM&R optimization concepts that will mobilize households to more actively save energy and take into account their comfort while implementing DSM&R. The following conclusions can be drawn from the presented analysis of the literature. The introduction of a DSM&R optimization algorithm in households based on the work schedule of individual appliances [15], determined on the basis of historical data specifying the trends in the work of these appliances, will not ensure optimal implementation of DSM&R. A better solution is to optimize energy consumption on-line using heuristic algorithms. This requires the use of two-way communication allowing change of the operating mode of the appliances planned to be switched on or off at a given moment. On the other hand, ensuring greater comfort for the individual consumer in the use of energy receivers during the implementation of DSM&R may take place by taking into account the parameters of smart appliances [30] and energy generated by RES in the optimization algorithm.