Energy and environment have become hot topics in the world. The building sector accounts for a high proportion of energy consumption, with over one-third of energy use globally. A variety of optimization methods have been proposed for building energy management, which are mainly divided into two types: model-based and model-free. Model Predictive Control is a model-based method but is not widely adopted by the building industry as it requires too much expertise and time to develop a model. Model-free Deep Reinforcement Learning(DRL) has successful applications in game-playing and robotics control. Therefore, we explored the effectiveness of the DRL algorithms applied to building control and investigated which DRL algorithm performs best. Three DRL algorithms were implemented, namely, Deep Deterministic Policy Gradient(DDPG), Double Deep Q learning(DDQN) and Soft Actor Critic(SAC). We used the building optimization testing (BOPTEST) framework, a standardized virtual testbed, to test the DRL algorithms. The performance is evaluated by two Key Performance Indicators(KPIs): thermal discomfort and operational cost. The results show that the DDPG agent performs best, and outperforms the baseline with the saving of thermal discomfort by 91.5% and 18.3%, and the saving of the operational cost by 11.0% and 14.6% during the peak and typical heating periods, respectively. DDQN and SAC agents do not show a clear advantage of performance over the baseline. This research highlights the excellent control performance of the DDPG agent, suggesting that the application of DRL in building control can achieve a better performance than the conventional control method. / Energi och miljö blir heta ämnen i världen. Byggsektorn står för en hög andel av energiförbrukningen, med över en tredjedel av energianvändningen globalt. En mängd olika optimeringsmetoder har föreslagits för Building Energy Management, vilka huvudsakligen är uppdelade i två typer: modellbaserade och modellfria. Model Predictive Control är en modellbaserad metod men är inte allmänt antagen av byggbranschen eftersom det kräver för mycket expertis och tid för att utveckla en modell. Modellfri Deep Reinforcement Learning (DRL) har framgångsrika tillämpningar inom spel och robotstyrning. Därför undersökte vi effektiviteten av DRL-algoritmerna som tillämpas på byggnadskontroll och undersökte vilken DRL-algoritm som presterar bäst. Tre DRL-algoritmer implementerades, nämligen Deep Deterministic Policy Gradient (DDPG), Double Deep Q Learning (DDQN) och Soft Actor Critic (SAC). Vi använde ramverket Building Optimization Testing (BOPTEST), en standardiserad virtuell testbädd, för att testa DRL-algoritmerna. Prestandan utvärderas av två Key Performance Indicators (KPIs): termiskt obehag och driftskostnad. Resultaten visar att DDPG-medlet presterar bäst och överträffar baslinjen med besparingen av termiskt obehag med 91.5% och 18.3%, och besparingen av driftskostnaden med 11.0% och 14.6% under topp och typisk uppvärmning perioder, respektive. DDQN- och SAC-agenter visar inte en klar fördel i prestanda jämfört med baslinjen. Denna forskning belyser DDPG-medlets utmärkta prestanda, vilket tyder på att tillämpningen av DRL i byggnadskontroll kan uppnå bättre prestanda än den konventionella metoden.

The subject of this diploma thesis is a construction technology project of a residential building in Frýdek. It is a residential building with four floors and a basement situated in Frýdek – Místek. The content of the preparation of technical reports for the construction and technological project studies and implementation of major technological stages. Creating a time and financial plan for a solved object. Further elaboration of technological regulations for drilled piles and reinforced concrete ceiling structure. A control and test plan is also being developed for these constructions. There is also a project site facility design and a machine assembly and a main lifting mechanism along with the occupational safety and health solution. In addition, the construction site is solved in connection with the transport routes. The thesis is designed with respect to the economic and time aspect.

The focus of this thesis is on the description of realization of administrative building with operational warehouses in Brno. The thesis consists of report on construction and technology, study of the realization of the main technological stages, technical report on site technologies, technical report on wider transportation connections, proposal for the machinery assembly, time schedule, detailed budget, technological guidelines for rein-forced concrete ceiling, control and exam plan for reinforced concrete, report on safety and health protection on construction site, environmental requirements, and in lastly also comparisons of material of contact thermal insulation system, crane proposal, noise study. The thesis consist 15 annexes.

The subject of thesis is construction technological project of B3 object, aiming at apartment building, which is the main building object. The apartment building consists of four above-ground floors and is established on foundation strips. The principal of thesis elaboration is the budget of the main object and the construction schedule. The diploma thesis also deals with the draft of construction site equipment for particular steps of construction, the construction organization, the lifting mechanism design, the technological regulation for selected operation, the transport accessibility and the protection of health and safety at work taking into account environmental protection.

The goal of this thesis is a building automation in which the circuits are controlled by a single-board computer Arduino and the entire system is operated by a mobile phone. The theoretical part describes a single-board computer Arduino, its parts, functions, capabilities and communication with it. In the practical part, the controlled circuits are described, implemented and a functional model of a house is created. The model of the house practically demonstrates behavior of the controlled circuits simultaneously with a possibility to manage and influence the system using a mobile phone.

The aim of this diploma thesis is trying to solve difficulties connected with administrative buildings. In this thesis we are talking about possibility that administrative building can have low energy footprint. Most of energy consumption of administrative building is not created by heating but mostly by cooling and by consumption of office equipment. That is why there have been used modern equipment of the building as well as automatic control of building equipment.