Thermal energy systems in buildings play a central role in global decarbonization efforts, accounting for a significant share of energy use and carbon emissions. This study addresses a key research question: how can advanced control strategies further enhance the performance of already energy-efficient, low-exergy thermal systems in low-energy buildings? To address this, a model predictive control (MPC) framework is designed to optimize the operation of an advanced thermal system based on modern concepts of low-temperature heating and high-temperature cooling, including ground-source heat pumps, borehole thermal storage, and modern air handling units. This approach employs a multi-layered MPC cost function, considering both immediate operational costs (electricity and heating) as well as system impact penalties, such as CO₂ emissions, thermal energy storage preservation, comfort violations, and peak load shaving, in response to fluctuating market cost signals, outdoor temperature, and thermal storage limitations. Applied to a validated, ultra-efficient commercial building, the MPC framework achieves a 13 % reduction in annual market-responsive operational costs, a 20 % improvement in long-term savings, and a four-year shorter payback period compared to existing well-established rule-based control. The results further confirm the robustness of predictive control under realistic forecast errors, as demonstrated by Monte Carlo simulations. From an environmental perspective, the CO₂ emission index stays below both Swedish electricity and district heating baselines, demonstrating the environmental benefits of predictive control through strategic sector coupling. Beyond the case study, the proposed method provides a scalable pathway for integrating predictive control into next-generation smart buildings. It highlights the potential of MPC as the final optimization layer in advanced thermal systems, aligning with global objectives for cost-promising and carbon-neutral building operations. 

Despite the exciting potential of microgrids in future smart energy systems, they encounter significant challenges, including fluctuations in energy demand and output, as well as the unpredictable behavior of electric vehicles. This article examines the ability of microgrids to enhance the integration of renewable energy sources to achieve Zero-Energy Buildings (ZEBs) and facilitate the deployment of Vehicle-to-Grid (V2G) technologies. The designed microgrid comprises vehicles utilizing V2G technology for daily energy storage and a hydrogen cycle featuring electrolyzers and fuel cells for seasonal storage. Probability functions based on uncertainty for distance, arrival, and departure periods from charging stations are formulated to mitigate uncertainties associated with electric vehicles (EVs). A genetic algorithm is employed to optimally regulate EVs' charging and discharging range and the hydrogen cycle's dynamic configuration. The system's feasibility is evaluated for a district in Tehran, characterized by a hot semi-arid climate per the Köppen climate classification, comprising 600 EVs and 3000 residential and 55 commercial buildings. The performance of the suggested smart system is compared with traditional scenarios from techno-ecological, economic, and environmental perspectives. The findings indicate that 62.6 % of the overall energy demand is met by renewable sources (wind and solar), and the microgrid can independently fulfill the need for over 50 % of the year, owing to the implemented hybrid optimum controllers. The findings indicate that 41 % and 16 % of total renewable electricity generation are stored in hydrogen systems and electric vehicles, respectively, highlighting their significant potential for both short-term and long-term storage. Compared to the same traditional scenarios, the suggested system, with an annual energy gain of 8.9 GWh, exhibits superior performance due to its little reliance on the grid while simultaneously ensuring the happiness of electric vehicle owners and the stability of energy storage systems. The intelligent microgrid demonstrates significant efficiency, conserving over 12,600 MWh of energy and decreasing more than 8800 tons of CO<inf>2</inf> emissions. Furthermore, this system generates a substantial financial benefit of approximately USD 468,000, highlighting its notable environmental and economic merits.

Natural ventilation has the potential to enhance indoor air quality in classrooms with elevated CO2 levels, although it may introduce outdoor pollutants. This study introduces a novel controller for automatic windows that simultaneously monitors outdoor air pollution and temperature, synchronizing window openings with mechanical ventilation system to create a comfortable, healthy, and energy-efficient indoor environment. The practicality of the proposed controller is assessed for a classroom in Delhi, Warsaw, and Stockholm, each with contrasting climates and outdoor pollution levels, specifically PM2.5 and NO2. The controller parameters are optimized for each city using a non-dominated sorting genetic algorithm (NSGA-II) to find the best trade-off between thermal comfort, CO2 levels, and energy consumption. The results show that the controller successfully met the indoor air quality standards in all cities; however, its operation was significantly influenced by the climate and pollution levels. While natural ventilation was utilized for 44% and 31% of the year in Warsaw and Stockholm, respectively, it was used for only 11% of the year in Delhi, the most polluted city. The optimization process significantly reduced energy use across all cities while also successfully reducing indoor CO2 concentrations. Although thermal comfort decreased slightly, it remained within acceptable thermal comfort conditions.

Heating and cooling account for about 55% of energy used in buildings worldwide and are a leading source of operational CO2 emissions. In cold‑climate regions such as Sweden, supplying this thermal demand efficiently and sustainably is crucial for meeting national and global climate goals. Low‑Temperature Heating (LTH) and High‑Temperature Cooling (HTC) systems, with minimal temperature difference between energy supply and demand, are modern solutions that work well with low‑exergy and renewable energy sources. When paired with Thermal Energy Storage (TES), especially deep seasonal boreholes, these technologies can boost energy flexibility, lower carbon intensity, and improve long‑term system resilience. Yet an important question remains: How can we push these already ultra‑efficient LTH‑HTC‑TES systems even further? The answer lies in adding intelligent control and optimization, the final layer needed to unlock their full operational, economic, and environmental potential.

This thesis investigates how smart design, advanced control, and optimization can make an already smart LTH‑HTC‑TES design even smarter. The study centers on Juvelen, a 10,000 m² commercial building in the city of Uppsala in Sweden, famous for its “deep‑green” thermal energy concept: it drills deep into the ground to utilize the ground's heating and cooling potential directly through borehole TES without any extra machinery while also using passive cooling, energy‑recovery ventilation, and smart interaction with the district‑heating network. This makes Juvelen a perfect testbed for exploring how AI‑driven optimization and control can further improve next‑generation thermal systems in commercial buildings. The research is carried out in three successive stages. First, a comprehensive literature review identifies the knowledge gaps in how TES is integrated, controlled, and optimized within LTH and HTC systems. Second, a detailed dynamic model of Juvelen and its thermal system is developed in TRNSYS and validated against real‑time data from the TEKLA building‑management system. Third, this validated model is used to investigate several smart performance‑enhancement approaches: (i) multi‑objective optimization with an artificial neural network (ANN) surrogate and evolutionary algorithms such as Grey Wolf, Non-dominated Sorting Genetic Algorithm‑II, particle swarm, and dragonfly, (ii) modifying the radiator temperature through an optimal adaptive radiator control fine‑tuned through particle swarm, (iii) evaluation of an alternative system configuration integrated with a ground‑source heat pump, and (iv) a forecast‑driven model predictive control (MPC) tested under realistic uncertainty.

The baseline study shows that, even without mechanical chillers or heat pumps, the existing system meets the entire cooling load and about one‑third of the heating demand directly through the borehole thermal storage, with the remaining heat supplied by the district heating network. Over its ten‑year payback period, the system avoids more than 140 tonnes of CO2, making it an ideal candidate for exploring how even the best-performing systems can be enhanced through smart design, predictive control, and optimization. Building on this foundation, the continuation of the research demonstrates how intelligent methods, and smart configurations can unlock further gains. Among several neural‑network training options, the Levenberg–Marquardt algorithm combined with a cascade‑forward architecture produced the smallest prediction errors and shortest computation times, making it the most dependable for optimization. Using this model, ANN‑based optimization lowered the levelized cost of thermal energy by 14.5%, reduced CO₂ intensity by 6%, and increased delivered thermal energy by 11%. An optimal adaptive radiator‑supply control, derived from a revised temperature equation considering solar radiation, ventilation status, and internal gains, improved comfort by 72.7% on the weighted temperature deviation scale and cut heating bought from the network by about 13.2%. Further benefits came from adding two 40 kW heat pumps, which increased seasonal flexibility. A new seasonal heat‑management scheme lets the district heating network recharge the boreholes each September, keeping the ground in thermal balance and boosting long‑term sustainability. This integration lowered annual operating costs by 9.4% and CO₂ impact from 23.9 tonnes in the existing system to 1.6 tonnes, aligning the proposed smart integration with Sweden’s net-zero building goals. Finally, a forecast‑driven model‑predictive controller achieved more than 13% and 5% extra market-responsive and operational cost savings and shortened payback by about four years, while Monte Carlo tests confirmed its robustness against forecasting errors.

Uppvärmning och kylning står för cirka 55% av den totala energianvändningen i byggnader globalt och är en av de främsta källorna till driftsrelaterade koldioxidutsläpp. I kallare klimatzoner, såsom Sverige, är det avgörande att tillgodose detta termiska behov på ett effektivt och hållbart sätt för att uppfylla både nationella och globala klimatmål. Lågtempererad uppvärmning (LTH) och högtempererad kylning (HTC), där temperaturskillnaden mellan energitillförsel och -behov är minimal, är moderna lösningar som passar väl ihop med lågexergi- och förnybara energikällor. När dessa kombineras med termisk energilagring (TES), särskilt djupa säsongslagrande borrhål, kan systemen erbjuda ökad flexibilitet, minskad koldioxidintensitet och förbättrad långsiktig driftsäkerhet. Trots detta kvarstår en viktig fråga i litteraturen: Hur kan redan ultraeffektiva LTH–HTC–TES-system förbättras ytterligare? Svaret ligger i att addera intelligent styrning och optimering – det sista skiktet som krävs för att frigöra deras fulla operativa, ekonomiska och miljömässiga potential.

Denna avhandling undersöker hur smart design, avancerad styrning och optimering kan göra ett redan intelligent LTH–HTC–TES-system ännu smartare. Studien fokuserar på Juvelen, en 10 000 m² stor kommersiell byggnad i Uppsala, känd för sitt "deep-green"-koncept där borrhål används direkt för att täcka värme- och kylbehovet utan värmepumpar eller kylmaskiner. Byggnaden är utrustad med passiv kylning, värmeåtervinning i ventilationssystemet och smart koppling till fjärrvärmenätet – vilket gör Juvelen till en idealisk testbädd för att undersöka hur AI-baserad styrning och optimering kan förstärka nästa generations termiska system i kommersiella byggnader. Forskningen bedrivs i tre steg: Först identifieras kunskapsluckor i litteraturen kring hur TES integreras, styrs och optimeras i LTH– och HTC-system. Därefter utvecklas en detaljerad dynamisk modell av Juvelens energisystem i TRNSYS och valideras mot realtidsdata från byggnadens styrsystem (TEKLA). Slutligen används den validerade modellen för att undersöka flera prestandaförbättrande metoder: (i) multiobjektiv optimering med artificiellt neuralt nätverk (ANN) och metaheuristiska algoritmer som Grey Wolf, NSGA-II, particle swarm och dragonfly, (ii) en adaptiv styrning av radiatortemperatur baserad på optimerad temperaturformel via PSO, (iii) utvärdering av ett alternativt system med integrerad bergvärmepump, och (iv) modellprediktiv styrning (MPC) baserad på prognoser och testad under osäkerhet.

Grundstudien visar att systemet, utan värmepumpar eller kylaggregat, täcker hela byggnadens kylbehov och cirka en tredjedel av värmebehovet direkt via borrhålen. Resten täcks av fjärrvärme. Under en återbetalningstid på tio år undviks mer än 140 ton CO₂, vilket gör systemet till en utmärkt kandidat för att undersöka hur redan högpresterande lösningar kan förbättras ytterligare genom smart design, prediktiv styrning och optimering. Byggt på denna grund visar fortsatta undersökningar hur intelligenta metoder och smarta konfigurationer ger ytterligare vinster. Levenberg–Marquardt-algoritmen i kombination med en cascade-forward-arkitektur visade lägst fel och snabbast beräkningstid bland flera ANN-träningsmetoder. Med denna modell kunde ANN-baserad optimering minska energikostnaden med 14,5 %, minska CO₂-intensiteten med 6 % och öka levererad termisk energi med 11 %. En nyutvecklad adaptiv styrstrategi för radiatorsystem – som tar hänsyn till solinstrålning, ventilation och interna värmelaster – förbättrade komforten med 72,7 % (baserat på viktad temperaturavvikelse) och minskade köpt värme från nätet med cirka 13,2 %. Ytterligare förbättringar uppnåddes genom att integrera två 40 kW värmepumpar, vilket ökade den säsongsvisa flexibiliteten. Ett nytt styrkoncept introducerades där fjärrvärmenätet laddar borrhålen varje september, vilket bibehåller termisk balans i marken och stärker långsiktig hållbarhet. Denna integrering sänkte de årliga driftskostnaderna med 9,4 % och CO₂-utsläppen från 23,9 ton till 1,6 ton per år, i linje med Sveriges klimatmål för nettonollbyggnader. Slutligen uppnådde den prognosbaserade MPC-styrningen mer än 13 % kostnadsbesparing på marknadsresponsiv drift och 5 % total driftskostnadsreduktion. Återbetalningstiden minskade med ungefär fyra år, samtidigt som robustheten mot prognosfel verifierades genom Monte Carlo-simuleringar.

The present article introduces and investigates a new approach for shaving the peak electricity demand and mitigating energy instability. At the heart of this concept is a smart integration for efficient hydrogen production/storage/usage to minimize energy costs and maximize the renewable penetration in the local electricity grid. The system is driven by a wind farm integrated with proton exchange membrane (PEM) electrolyzers and reverse osmosis desalination units for efficient electricity, hydrogen, and freshwater production. It also combines with PEM fuel cells equipped with a hydrogen tank to meet the demand constantly when renewable electricity is unavailable or unstable. The system's practicality is assessed and compared for various Swedish cities with high wind potential from thermodynamic, economic, and environmental aspects to see where it works effectively. The comparative results of various scenarios show that integrating 32 wind turbines, 2 electrolyzers, and 2 reverse osmosis units, with 25% of electricity going to electrolyzers, 20% to reverse osmosis, and 55% to the grid, is the most optimal configuration/allocation. Optimal locations for the power plant are identified in Visby, Halmstad, and Lund due to favorable wind conditions. Setting up the system in Visby could prevent 1878.2 tonnes of CO2 emissions, generate 93,910 MWh of electricity annually, and create 213 ha of green space. The proposed system in Visby could boast the biggest electricity generation capacity, reaching 11,263 MWh, sufficient to power 938 households. Scaling this model to 12 cities in Sweden could provide the electricity needs of 4500 households, demonstrating the potential for widespread impact.

This article presents and thoroughly examines an innovative, practical, cost-effective, and energy-efficient smart heating, ventilation, and air conditioning (HVAC) system. The fundamental component of this concept is a stateof-the-art method called Deep Green Cooling technology, which uses deep drilling to utilize the ground's heating and cooling potential directly without the need for machinery or heat pumps. This method satisfies demands with the least energy use, environmental impact, and operational costs. In order to effectively oversee and regulate energy production, storage, and utilization, the system consists of an intelligent control unit with many smart controllers and valves. Renewable energy deployment is made easier, and the intelligent automation unit is more compatible with the help of a high-temperature cooling resource with a high supply temperature of 16 degrees C. The technical, environmental, and financial aspects of the suggested smart office building system in the southern region of Uppsala, Sweden, are evaluated using TRNSYS software. According to the results, boreholes provide more than 28.5 % of the building's energy requirements by utilizing the ground's ability to generate affordable, dependable seasonal thermal energy. The district heating network satisfies the remaining demand, amounting to 787.2 MWh, highlighting the benefits of combining conventional and renewable energy sources for increased supply security and dependability. The borehole thermal energy storage system meets the building's entire cooling need, underscoring the importance of high-temperature cooling systems. The most expensive part of the system is the borehole thermal energy storage, which accounts for over half of the total investment. The system has an appropriate payback period of ten years, proving its long-term profitability and cost-effectiveness, thanks to removing the machinery and heat pump. With 3138 MWh of ground-source heating and cooling, the system saves 17,962 USD by reducing CO2 emissions by about 143.7 t, sufficient to grow 16.3 ha of trees throughout the payback period.

This study introduces a novel energy conversion and management framework to reduce carbon emissions in the energy sector and expedite the global shift towards sustainable practices. The system is driven by biomass-based solid oxide fuel cells for efficient power generation. Central to this approach lies the integration of additional hydrogen injection provided by a thermally-driven vanadium chloride cycle, aiming to enhance the quality of the syngas entering the fuel cells. The system is also combined with a super-critical CO2 cycle that generates power by passively enhancing performance through flue gas condensation. The proposed model's feasibility is evaluated in depth, techno-economically, considering thermodynamics and specific cost theories. As part of artificial intelligence, a neural network model is coupled with the genetic algorithm to determine the best operating status while minimizing computation time. According to the results, the suggested new integration results in higher efficiency and lower cost than a similar system without hydrogen injection. The results further show that the triple-objective optimization achieves output power, second-law efficiency, and overall system cost of 3425 kW, 48.5 %, and 2.3 M$/year, respectively. Eventually, the gasifier is the main contributor to the highest level of exergy destruction, and fuel utilization and current density are the most important parameters in modeling.

This paper introduces a novel heating, ventilation, and air conditioning (HVAC) system in cold climates to meet thermal comfort with lower energy use than traditional systems. The smart HVAC unit is driven by a naturally driven free heating and cooling system using earth as a seasonal thermal reservoir to store/generate energy through 25 boreholes to meet the heating/cooling demands of an office building in Uppsala, Sweden. TRNSYS analyzes the system's performance, applying thermodynamic rules and lifecycle cost assessment. The results show that the air handling unit can meet more than 50% of heating and cooling demands. Despite high investment costs, the proposed system has a promising payback period of fewer than ten years, demonstrating the role of smart HVAC design. Considerably lower primary energy is used by recovering the free heating and cooling of 313 MWh from the ground, highlighting the effectiveness of utilizing the earth's stable temperatures.

Efficient heating, ventilation, and air conditioning (HVAC) systems play a crucial role in preserving ideal indoor air quality by utilizing modern energy-saving techniques while ensuring environmental integrity. This paper introduces a novel HVAC system in cold climates to meet thermal comfort with lower energy use than traditional systems. The smart HVAC unit is driven by a naturally driven free heating and cooling system interacting with the district heating network. At the heart of this concept is using earth as a seasonal thermal reservoir to store and generate energy through 25 boreholes equipped with several smart controllers to meet the heating and cooling demands of an office building in Uppsala, Sweden. TRNSYS analyzes the system's performance from the techno-economic aspects, applying thermodynamic rules and lifecycle cost assessment. The results show that the air handling unit can meet more than 50% of heating and cooling demands throughout the year thanks to efficient waste heat recovery via the proposed intelligent integration. According to the results, despite high investment costs of around 399,000 $, the proposed system has a promising payback period of fewer than ten years, demonstrating the role of smart HVAC design through a clever control framework. Finally, the results show that considerably lower primary energy is used by recovering the annual free heating and cooling of 313 MWh from the ground via boreholes, highlighting the effectiveness of utilizing the earth's stable temperatures for thermal control.

The present article proposes a novel smart building energy system utilizing deep geothermal resources through naturally-driven borehole thermal energy storage interacting with the district heating network. It includes an intelligent control strategy for lowering operational costs, making better use of renewables, and avoiding CO2 emissions by eliminating heat pumps and cooling machines to address the heating and cooling demands of a commercial building in Uppsala, a city near Stockholm, Sweden. After comprehensively conducting techno-environmental and economic assessments, the system is fine-tuned using artificial neural networks (ANN) for optimization. The study aims to determine which ANN design and training procedure is the most efficient in terms of accuracy and computing speed. It also assesses well-known optimization algorithms using the TOPSIS decision-making technique to find the best trade-off among various indicators. According to the parametric results, deeper boreholes can collect more geothermal energy and reduce CO2 emissions. However, deep drilling becomes more expensive overall, suggesting the need for multi-objective optimization to balance costs and techno-environmental benefits. The results indicate that Levenberg-Marquardt algorithms offer the optimum trade-off between computation time and error minimization. From a TOPSIS perspective, while the dragonfly algorithm is not ideal for optimizing the suggested system, the non-dominated sorting genetic algorithm is the most efficient since it yields more ideal points rated below 100. The optimization yields a higher energy production of 120 kWh/m2, as well as a decreased levelized cost of energy of 57 $/MWh, a shorter payback period of two years, and a reduced CO2 index of 1.90 kg/MWh. The analysis reveals that despite the high investment costs of 382.50 USD/m2, the system is financially beneficial in the long run due to a short payback period of around eight years, which aligns with the goals of future smart energy systems: reduce pollution and increase cost-effectiveness.