# Simplified Energy Model and Multi-Objective Energy Consumption Optimization of a Residential House
###### Abstract  
Some analyses state that buildings contribute to overall energy consumption by 20-40%, which, in the context of the recent geopolitical energy crisis, makes them a critical issue to study. Finding solutions for better energy management in buildings can have a significant impact on the energy sector, thus reducing EU energy dependencies and contributing to the fulfillment of the REpowerEU goals. This paper focuses on proposing a simplified model of a residential house considering the main appliances, heating and cooling, a photovoltaic system, and electric vehicle recharging. Weather and solar irradiance forecasts are taken into account. The model predicts the energy demands of a house based on online weather forecasts and the desired indoor temperature. The article also focuses on the analysis of how weather forecast uncertainty affects energy demand prediction. This model can be used to better understand and predict the energy demand of either a single house or a set of houses. A multi-objective optimization approach that takes into account the preferences of users/inhabitants is developed to provide a compromise between the price paid for the electricity and temperature comfort. The authors plan to apply the proposed model to a residential house's real-time control system. The model will be tuned, its predictions will be tested, and it will be used for energy demand optimization. os 2  
peaks of this demand during the daytime [5]. However, their storage potential is also large, which might also be used for grid stabilization [6]. A better control strategy can be based on the scheduling of home appliances or more effective renewable energy usage together with energy storage and battery management [7; 8; 9; 10; 11]. People need to be accounted for in the models, not only because the models need information about the occupancy in buildings, but mainly because people have their preferences, for example with regard to thermal comfort. Most articles mention the unreliability of models and predictions. Several articles focus on model predictive control (MPC) [1; 3; 4; 12; 13; 14] that takes into account the comfort of the residents as well as different types of constraints or uncertainty of predictions. An overview of different state-of-the-art MPC approaches used in buildings, together with weather forecasts and an energy storage system, can be found in [15]. For example, the authors mention different operating strategies of the controller: \"demand shaving, demand shifting, energy consumption minimizing, energy cost minimizing, maximal use of renewable energies, etc.\". Some articles focus on the possibilities of optimization [9; 11; 15; 16; 17; 18; 19], predictions of energy demands/consumption or weather and solar forecasts [11; 13; 15; 20], or the building model where the dynamics are mostly caused due to the heating and cooling systems [9; 13; 14; 15; 17; 18; 19; 21; 22; 23; 24; 25; 26]. Demand response programs can then be used to obtain better control of the energy demands as needed, so as to retain the energy grid stability. There are many articles that focus on the demand response initiative, for example [10; 11; 27; 28]. Demand response methods are mainly used from the perspective of aggregators, to shave the peaks of energy demand. The energy demands of industrial customers seem to be more predictable than that of residential customers [29], but studies that are mentioned above focus on individual consumers. Demand response initiatives will need a well-working infrastructure of control systems in individual buildings. Two-way communication will take into account not just the appliances in the building, but also the preferences of the residents. Most of the above-mentioned methods and approaches require an energy model of the house. In practice, there are three different approaches to model design, which are models based on physical principles (white box), models based on experimental data (black box) and models using a combination of physics and data (grey box).