# Improved Structural Local Thermal Energy Planning Based on Prosumer Profile: Part A
## 1 Introduction  
The building sector accounts for almost 40% of the world's energy consumption [1, 2]. Thermal energy exploits 75% of that consumption, acting as the most dominant vector of energy consumption worldwide [3]. One significant aspect of the sector is the creation of local coalitions of energy that aim to reduce their carbon footprint [4]. Local energy communities (LECs), as they are described, gained legal and technological grounds in the last 10 years due to the part energy consumers play in the transition of the energy map [1, 5, 6]. However, the creation and operation of thermal LECs (TLECs) raise infrastructural and economic issues mainly because of the lack of regulatory framework, funding schemes, and proper tools for planning, creating, and maintaining LECs [7, 8, 9, 10, 11, 12]. The consumers of such communities play an intricate part in the synthesis problem as they can both produce and consume renewable energy at different rates depending on the available equipment and their level of energy demand [13, 14]. Therefore, incentives and research for suitable tariff regimes and energy management should be of utmost importance in order to accelerate prosumers' penetration to the market [15]. Their consumption and production profiles define the synthesis of the complex thermal energy production layouts and act as the bedrock of the superstructure, which is the final layout of the system [8, 16]. To successfully create prosumer-centered solutions for energy systems, parameters such as consumption profiles, prosumers with similar schedules and behaviors, and social aspects of behavior change towards energy consumption need to be considered. [PERSON] et al. designed a heuristic algorithm which grouped prosumers with similar interests, locality, and consumption patterns and placed them into clusters. The results suggest that load rescheduling could make feasible a more decentralized community that is less dependent on the main grid [17]. The importance of prosumers' energy behavior and profiling was also investigated by [PERSON] et al. In their work, they assessed the demand-side management of energy communities and its impact on the energy balance with different mixtures of prosumer profiles. Their study concluded that shifting the loads to several prosumers might offer a viable solution, but it undermined the global optimum choice due to the absence of appropriate classes for prosumers with similar profiles [18]. [PERSON] et al., in a novel study, identified the inherit flaw of abstract load shifting without taking into account the prosumers' individual needs. Their multiobjective optimization algorithm aimed to minimize the cost and the discomfort of the prosumers under flexible load shifting to increase RES penetration [19]. The main issue with the approaches in the available literature appears to be the segmentation and classification of the prosumers in TLECs, which will provide significant computational and energy management insight. Additionally, the energy systems that provide energy are undergoing a significant transition as more renewable-based systems contribute to their synthesis [20]. Therefore, much effort is put into the optimum synthesis of such systems in order for them to meet demand despite the intermittent nature and availability of renewable energy resources [16]. The available tools for designing such systems are categorized either as heuristic-based approaches or optimization-based [16]. The heuristic approaches depend on the expertise of the system's designer to find a working solution and refer to simulations to determine the validity of the selection [21; 22; 23; 24]. On the other hand, optimization approaches are well-established as they propose several layouts for the same system, providing a repetitive loop of checking and verifying the optimum solution [25; 26; 27; 28]. However, the optimum solution might be overlooked in complex systems mainly because of the plethora of semioptimum choices [16].