Despite the growing number of scientific investigations on developing taxonomies in various industries and categorizing energy end-use (EEU) processes, research on such frameworks in the data center (DC) sector remains limited, restricting systematic analysis of energy use, benchmarking, and identification of energy efficiency measures. This study seeks to achieve two key objectives: (1) to create a hierarchical taxonomy for categorizing EEU process, (2) and to identify the most relevant energy performance indicators (EnPIs). The methodology combines hierarchical classification of energy flows with quantitative analysis based on time-resolved operational data, allowing for detailed analysis of dynamic system behaviour and allocation of energy use across processes, applied to a DC room with a detached in-rack water cooling system in Sweden operated by Ericsson. The results of this paper present a novel hierarchical taxonomy for a water-cooled DC room with six defined EEU processes, of which four are related to data processing processes and two to support processes. Notably, over 99.9% of total EEU is connected to data processing processes which is a significantly higher figure compared to other production processes in other sectors. Moreover, the established EnPIs can facilitate benchmarking between DCs with similar infrastructures and for monitoring the energy use of various EEU processes. The proposed methodology is transferable to other DC environments with comparable system boundaries and data availability, and its reproducibility is supported by the structured and data-driven methodology, which enables replication of the analytical approach given comparable data availability.

Projektet har undersökt potentialen för att använda Direct Air Capture (DAC) integrerat i ventilationssystem för att fånga in metabolisk och atmosfärisk CO₂ i universitetsmiljöer. En uppgraderad laboratoriebaserad testanläggning med zeolit 13X har utvecklats, där särskilt fukthaltens inverkan på infångningseffektiviteten har studerats genom installation av en avfuktare som möjliggör kontrollerad styrning av luftfuktigheten.

De experimentella resultaten visar att reducerad fukthalt är avgörande för hög infångningsgrad, där en relativ fuktighet om cirka 8 % möjliggjorde en CO₂-infångning på över 90 %, jämfört med omkring en tredjedel utan avfuktning. 

Den tekno-ekonomiska potentialbedömningen omfattar samtliga campusområden vid Linköpings universitet och visar en total årlig infångningspotential om 8 031 ton CO₂. Den tillhörande energianvändningen uppgår till 4 015 MWh el och 12 046 MWh värme per år, vilket motsvarar energikostnader på 10 MSEK per år. De potentiella intäkterna från försäljning av infångad CO₂ beräknas till 20 MSEK per år, vilket ger en årlig avkastning på 10 MSEK när energikostnaderna beaktas. Även om installationskostnader för DAC-enheterna och möjliga intäkter från koldioxidkrediter inte ingår i beräkningarna, indikerar resultaten att DAC-teknik kan utgöra en betydelsefull komponent i arbetet mot koldioxidneutrala universitetsmiljöer.

The project investigated the potential of using Direct Air Capture (DAC) integrated into ventilation systems to capture metabolic and atmospheric CO₂ in university environments. An upgraded laboratory-based test facility using zeolite 13X was developed, with particular emphasis on studying the impact of humidity on capture efficiency through the installation of a dehumidifier that enables controlled regulation of air moisture levels.

The experimental results demonstrate that reduced humidity is critical for achieving high capture efficiency. A relative humidity of approximately 8% enabled CO₂ capture rates exceeding 90%, compared with around one-third without dehumidification. The techno-economic assessment included all campus areas of Linköping University and shows a total annual capture potential of 8,031 tonnes of CO₂. The associated energy use amounts to 4,015 MWh of electricity and 12,046 MWh of heat per year, corresponding to annual energy costs of approximately SEK 10 million. Potential revenues from the sale of captured CO₂ are estimated at SEK 20 million per year, resulting in an annual net return of SEK 10 million when energy costs are considered. Although installation costs for the DAC units and potential revenues from carbon credits were not included in the analysis, the results indicate that DAC technology can represent a significant component in efforts toward carbon-neutral university environments.

Interdisciplinary teams skilled in Aristotle’s three knowledge forms, phronesis, episteme, techne, are essential for dealing with complex challenges in future. As sectors transition to new concepts, risks exist for unhinged paradoxes, i.e., unforeseen consequences. This is particularly relevant for industries with complex infrastructures, such as data centers (DCs). This paper presents a conceptual roadmap consisting of four interconnected phases for integrating energy efficiency concepts in DCs, with a focus on avoiding unhinged paradoxes. The knowledge creation process during the roadmapping process underscores the importance from (1) the acknowledgement of an external threat, (2) inclusion of an interdisciplinary team, and (3) regular project meetings to facilitate a collaborative and interdisciplinary environment. Additionally, the insights gained from this research extend to sectors beyond DCs and can be applied to other industries working towards increased energy efficiency. 

Climate change is increasingly affecting the water cycle, with droughts and floods posing significant challenges for agriculture, hydropower production, and urban water resource management due to growing variability in the factors influencing the water cycle. Reinforcement learning (RL) has demonstrated promising potential in optimization and planning tasks, as it trains models on historical data or through simulations, allowing them to generate new data by interacting with the simulator. This systematic literature review examines the application of reinforcement learning (RL) in water resource management across various domains. A total of 40 articles were analyzed, revealing that RL is a viable approach for this field due to its capability to learn and optimize sequential decision-making processes. The results show that RL agents are primarily trained in simulated environments rather than directly on historical data. Among the algorithms, deep Q-networks are the most commonly employed. Future research should address the challenges of bridging the gap between simulation and real-world applications and focus on improving the explainability of the decision-making process. Future studies need to address the challenges of bridging the gap between simulation and real-world applications. Furthermore, future research should focus on the explainability behind the decision-making process of the agent, which is important due to the safety-critical nature of the application.

In our digitalized world, Data Centers (DCs) serve as crucial infrastructure. Within the DC sector, data processing operations, including processes such as process cooling, hold special significance when investigated from an energy efficiency perspective, as they account for a substantial portion of total energy end-use. Therefore, it is important to prioritize data processing operations in energy management. The objective of this research is to explore the application of AI-powered clustering techniques to identify cooling operational statuses. Additionally, this research offers valuable perspectives on using AI for visualizing and identifying cooling patterns that deviate, which can provide valuable insights into DC energy management. The study object consists of a DC room equipped with Liquid Cooling Packages (LCPs). The findings show that the cooling power density on average is 9.1 kW/m 2 . Through analysis of the elbow curve, the optimal number of clusters is identified to be three. One of the identified clusters, i.e., Cluster 3, is characterized by large time periods with no supplied cooling from the LCPs. When comparing Clusters 1 and 2, Cluster 1 has a higher temperature difference between the chilled water supply and return, but a lower LCP return temperature compared to Cluster 2. Moreover, the quantified cooling characteristics contribute to the understanding of the LCPs’ operational statuses and cooling performance, which is useful for implementing targeted improvements, e.g., adjusting PID parameters, in the cooling infrastructure.

Direct Air Capture (DAC) technologies have emerged as a promising solution to address climate change and meet global climate goals. However, despite the importance of DAC in designing carbon-negative buildings, there is a lack of research focusing on the energy and cost aspects in building ventilation systems. The objective of this research is to investigate the CO2 capture potential and economic viability of integrating small-scale DAC into a building ventilation system integrated within a gym space. A gym space located in the city of Linköping, Sweden, is used as the research object. Furthermore, the study investigates the CO2 capture potential across a portfolio of gym spaces corresponding to an area of 24,760 m2. The results show that the CO2 capture potential varies between 54 kg/day and 83 kg/day for the investigated gym space. Moreover, the total CO2 capture potential is between 588 ton CO2/year and 750 ton CO2/year for the portfolio of gym spaces. The results also demonstrate that regenerating the sorbent during non-operating hours is more energy-efficient and economically advantageous compared to performing four complete regeneration cycles during operating hours. Based on a sorbent capture potential of 0.2 mmol/g and 2.0 mmol/g, and a CO2 price of 1,000 SEK, the break-even price for energy is 0.25–0.53 SEK/kWh. Lastly, the research shows that, among the investigated cases, the only economically viable solution corresponds to sorbent capture potential 2.0 mmol/g and utilizing low-grade heat for the generation process, resulting in a total cost of 663 SEK/ton CO2.

The need to understand how a warmer climate affects the power and energy demand in cold countries is important for urban planners and policymakers. By using data from utility bills that are commonly available today, together with outdoor temperatures, it is possible to analyze historical and future power and energy demand. The scientific value of this research includes the development of a methodology to explore effects on future heat demand in the Nordic region based on a combination of historical data, building properties, and predictions of future climate. This is achieved by using an energy signature model and regression analysis. Seventy multi-family buildings in Linköping, Sweden, are investigated from 1980 to 2050. The results show that the effects from historical variations in internal heat gains (average annual increase of 1 %) on the specific energy use for space heating (SPH) is minor for the district, i.e., less than 2 % when comparing 2020 and 1980. The opposite is found for variations in outdoor temperatures, where the average specific energy use is predicted to decrease by about 25 % in 2050 compared to 1980, with the used forecast of future climate. This corresponds to a decrease from 127 kWh/(m2·year) to 93–96 kWh/(m2·year). Additionally, the maximum heating power demand of the district is predicted to decrease by about 30 %, from 4,855 kW in 1980 to 3,468 kW in 2050. In conclusion, our results demonstrate a strong effect of decreased SPH and heating power demand in residential districts due to a warmer climate.

Machine Learning (ML) is frequently utilized in prediction tasks; however, its applications in hydropower forecasting,particularly in forecasting hourly power production, has not been thoroughly investigated. In this paper, two Deep Learning (DL) models,namely an autoregressive neural network and Long Short-Term Memory, are compared to a seasonal autoregressive moving average(SARIMA) model to forecast the hourly power production at a hydropower station situated in Linköping, Sweden. Hyperparameteroptimization algorithms are used to identify suitable DL models and algorithms for automatic model identification of SARIMA modelsare utilized. The three models are evaluated using a rolling origin strategy on a test dataset that consists of 10 months (January – October2023) of hourly power production. The DL models provided similarly accurate forecasts as the SARIMA model according to meansquared error and mean absolute error. However, the DL models are poorly calibrated, resulting in lower coverage compared to theSARIMA model. Furthermore, the models are using a univariate time series (i.e., using historical power production to forecast futurepower production) and future studies need to explore additional variables that may be useful in providing a more accurate forecast.

In the era of society’s ongoing digitization and the exponential growth in data volume, alongside a growing energy demand, energy management plays an integral role in data centers (DCs) and is a key factor in the quest for decarbonization. In light of the complex nature of DCs, traditional energy management strategies are inadequate. This research introduces a data-driven decision-making framework for DCs, grounded in the OODA (Observation, Orientation, Decision, and Action) loop and based on insights from an Ericsson-operated DC in Linköping, Sweden. The developed framework enables DCs to enhance energy efficiency effectively. Rooted in the OODA loop and leveraging extensive datasets from DCs’ building management systems, this framework aids in decreasing cooling energy usage through strategic, data-driven decision-making. By adopting AI methods, specifically K-means clustering in this research, for continuous monitoring and fine-tuning (Proportional, Integral, Derivative) PID parameters, the framework aids in improving operational efficiency.

Information Communication Technology (ICT) centers play a vital role as essential facilities within our digitalized society. Energy efficiency holds great significance in the ICT sector, driven by the rising energy costs and to reduce the environmental impact. Simultaneously, it is essential to ensure a sufficient cooling supply for servers. Artificial Intelligence (AI) can be used to analyze patterns in large datasets, facilitating valuable insights that are difficult for humans to analyze alone because of the complexity and size of the datasets. The aim of this research is to characterize cooling patterns and explore how AI-driven clustering algorithms can be used to identify cooling operational statuses. The research object is an ICT room situated in Linköping, Sweden, and operated by the global telecommunications company Ericsson AB. The ICT room has Liquid Cooling Packages (LCPs) for water-based cooling.The results show that the average cooling power density in the ICT room is 6.98 kW/m2, and the interquartile range is 8.26 kW/m2. The results also demonstrate the potentialities in using AI-based clustering algorithms, K-means in the presented research, to uncover insights related to cooling operational statuses. Furthermore, the results show that it is suitable to divide the data points into four clusters, providing a detailed description of the characteristics of the dataset. The identified clusters differ with regards to variables, among other, such as LCP return air temperature and temperature difference between chilled water supply and return. This is beneficial in identifying undesired operational statuses of LCPs, e.g., low temperature difference between chilled water supply and return, which is an indicator of a poor cooling performance.