Thermal energy storage (TES) is recognized as a well-established technology added to the smart energy systems to support the immediate increase in energy demand, flatten the rapid supply-side changes, and reduce energy costs through an efficient and sustainable integration. On the utilization side, low-temperature heating (LTH) and high-temperature cooling (HTC) systems have grown popular because of their excellent performance in terms of energy efficiency, cost-effectiveness, and ease of integration with renewable resources. This article presents the current state-of-the-art regarding the smart design of TES integrated with LTH and HTC systems. TES is first explained in basic concepts, classification, and design possibilities. Secondly, the literature on well-known existing control approaches, strategies, and optimization methods applied to thermal energy storage is reviewed. Thirdly, the specifications, types, benefits, and drawbacks of the LTH and HTC systems from the viewpoints of supply and demand sides are discussed. Fourthly, the smart design of TES integrated with the LTH and HTC systems based on the control approach/strategy, optimization method, building type, and energy supplier is investigated to find the newest technology, ideas, and features and detect the existing gaps. The present article will provide a realistically feasible solution for having a smart storage configuration with the maximum possible energy efficiency, reliability, and cost-effectiveness for the building owners and the energy suppliers.

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.

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.

The present study introduces and thoroughly investigates a novel smart heating, ventilation, and airconditioning system with thermal storage in a newly built commercial building located in Uppsala,Sweden. The system combines 25 double U-tube borehole thermal energy storages, district heating, andintelligent control strategies to effectively manage office and restaurant heating and cooling demands.A novel optimal adaptive control framework dynamically adjusts the supply temperature of the radiatorsby considering solar radiation, ventilation flow rate, occupancy gains, and outdoor temperature. Thesemodifications are optimized with the particle swarm method, targeting enhanced thermal comfort andenergy efficiency. The proposed framework is compared with the existing control system based solelyon outdoor temperature, and we report significant techno-economic, environmental, and comfortindicators. We show that the outdoor temperature history and wind velocity have minimal effects onheating demand deviations, while solar radiation, occupancy gains, and ventilation performance playsignificant roles. The results further indicate that solar radiation is the most influential factor in warmermonths, whereas occupancy and ventilation gain are more important in colder months. Resultsdemonstrate substantial enhancements in thermal comfort, with the weighted temperature deviationindex reduced by 72.7% and the comfort consistency ratio increased by 54.4%. The designed adaptivecontroller reduces the annual heating supplied to radiators and payback period by 13.2% and 9% anddecreases CO₂ emissions and index by 9.4% and 2.6%, respectively. After 20 years, the adaptivecontroller outperforms the basic model in terms of profit, increasing it by 20.4% to 190,260 USD,proving its economic superiority in the long run. In transitional months like April (14.9 MWh, 56.3%of the total) and May (15.9 MWh, 69.9%), when efficient solar gains reduce heating demands, thesuggested adaptive controller also has substantial monthly energy savings.

Aligned with UN Sustainable Development Goal 7, which aims to ensure access to affordable, reliable,sustainable, and modern energy, the shift towards sustainable and energy-efficient heating and coolingsystems is essential for minimizing carbon emissions and operational expenses in buildings. Seasonalborehole thermal energy storage has emerged as a viable alternative for renewable deployment, yet itscomplete potential is often underexploited in existing designs. This article investigates the feasibility ofadding a heat pump system through a highly dynamic control framework to a commercial building inUppsala, Sweden, operating based on free heating and cooling interacting with the district heatingnetwork. A comparative benchmarking analysis is carried out to evaluate the impact of heat pumpintegration on performance effectiveness, short- and long-term financial viability, and CO₂ emissions.The results indicate that the potential of costly borehole thermal energy storage, which accounts formore than 50% of the total investment cost, is not fully utilized in the existing system. Integrating aclever heat pump system enhances the annual heat extraction from the ground by approximately 27%,resulting in a 29% decrease in overall heating costs, albeit the increased electricity purchased from thegrid up to 100,000 SEK/year. This modification raises initial investment costs by 456,407 SEK,representing an 11.1% increase relative to the existing system. It also requires a more complexconfiguration, including a borehole heat charging strategy in September to mitigate long-term groundtemperature reduction. However, the results show that the heat pump integration reduces the payback,resulting in a 20% enhancement in long-term savings compared to the net present value of the existingsystem over 20 years. From the environmental point of view, the alternative system demonstratesgreater alignment with zero-emission goals, resulting in a net CO₂ impact of only 1.6 tonnes annually,compared to 23.9 tonnes in the existing system due to the high deployment of renewables, increasedconversion efficiency (4.7 times heat utilization per unit of electricity), and more reliance on electricitygrid which is greener than district heating in Sweden.

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.