Sweden implemented a unique policy instrument over two consecutive five-year periods aimed at promoting industrial energy efficiency: a voluntary agreement programme (VAP) known as the Programme for Improving Energy Efficiency in Energy-Intensive Industries (PFE). This paper evaluates the second programme period using process and impact evaluation approaches. The impact evaluation revealed that the programme generated numerous benefits beyond the implementation of energy efficiency measures. The second programme period demonstrated impacts comparable to the first, underscoring the pivotal role of energy management within policy frameworks. Moreover, the findings suggest that VAPs may be among the few policy instruments capable of achieving substantial impact within industrial production processes. The process evaluation identified two critical gaps: a policy gap and a knowledge gap. Many of the positive elements present in the evaluated programme are currently absent from Sweden’s industrial policy mix. The role of the policy operator—as facilitator or intermediary—proved essential, particularly in the context of policies involving energy audits and certified energy management systems. In this case, the Swedish Energy Agency played a central role. The study concludes that continuous energy management, when integrated throughout the entire organization, significantly enhances companies’ capacity to identify and implement energy efficiency measures. This approach not only deepens internal knowledge but also fosters the development of more structured and effective routines. Furthermore, the findings suggest that financial incentives may be essential to ensure the long-term persistence of energy efficiency practices. Consequently, future policy design should include a strong facilitator and incorporate mechanisms that both reinforce organizational knowledge of energy end-use processes and promote sustained engagement over time, preferably involving a financial incentive.

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.

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.

The ongoing switch towards more sustainable energy system is one of the most important challenges that society today isfacing. The energy system in Sweden is currently confronted with questions as for example unprecedented electricity prices driven bothby geopolitical constrains and supply constrains in the European energy system. Consumers have also experienced significant differencein price within the country, and existing bottlenecks have resulted in substantial income transfer from the consumers to the transmissionsystem operator. This situation is mainly based on the fact that most of the production of electricity occurs in the north of Sweden, whilstthe demand is relatively higher in the south of the country. Sweden faces a delicate balance between increasing electricity demand andthe need for sustainable, efficient, and resilient energy systems. The energy system in Sweden needs to be resilient and at the same timemeet the upcoming significant increased demand of electricity. It is vital that all available energy sources are included in planning of thefuture energy system. Sweden has a higher use of electricity compared to other European countries, mainly due to historical low electricityprices. This means that there is a potential to reduce the use of electricity in Sweden, which needs to be considered to avoid risk to missthe potential of more efficient use of electricity. There are several studies that are analysing the most optimal mix of electricity production.The aim of this study is to give a review of current research studies dealing with the opportunities and challenges linked to the need fora future resilient Swedish energy system that meets both the today´s and futures need of electricity.

The complexity of the electricity system is increasing due to various transitions and events taking place within and outsideof the electricity market such as increased loads from distributed power supplies. The risk for various disturbances may increase withthese transitions and events, including non-electricity system related disturbances like climate change. There is an urgent need to improveresilience of the electricity system so that it can handle also low probability and high impact disturbances. The objective of this paper isto analyse seven resilience principles, originally developed for socio-ecological systems, and interpret them for the electricity system.Results from the analysis indicate that the resilience principles can be seen to represent different categories in the socio-technical systemthat is the electricity system. These categories are technology, learning, information, stakeholder, organisation, and governance. Theresilience principles enable a holistic view of the electricity system, and they can function as a support during the work to increaseresilience of the electricity system.

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.

Denna rapport är ett försök att sammanfatta den forskning som har gjorts inom området energieffektivisering av industriell belysning med fokus på teknik, hinder, drivkrafter och mervärden. Rapporten har skrivits inom ramen för projektet Mervärden vid energieffektivisering – för en ökad energieffektivisering inom industriell belysning, som finansierats av Energimyndigheten. Målgruppen är vetenskapssamhället i Sverige, universitetsstudenter med energi- och miljöinriktning och industriföretag.

Resultaten från litteraturstudien visar att den forskning som har gjorts inom området energieffektivisering av industriell belysning och dess potentiella mervärden är förhållandevis begränsad. Vad författarna kan se så saknas exempelvis studier som tar ett helhetsgrepp på mervärden. Det saknas även forskning om metoder för kvantifiering av mervärdens ekonomiska betydelse.

När det gäller forskning som relaterar till styrning och optimering av belysning så finns en del studier med fokus på bostäder, offentliga lokaler och gatubelysning. Forskning som studerar styrning och optimering av industriell belysning lyser med sin frånvaro. Det ska sägas att studierna av bostäder, offentliga lokaler och gatubelysning innehåller metoder som skulle kunna vara användbara även för industriell belysning. Några exempel på områden är prediktivt underhåll för industriell belysning samt optimering och styrning av belysning tillsammans med andra stödprocesser såsom luftkonditionering.

Styrning och optimering av belysning underlättas om det finns infrastruktur för digital teknik, det vill säga en infrastruktur som tillhandahåller sensorer, datakommunikation, datalagring och dataanalys. Om denna infrastruktur inte finns tillgänglig i tillräcklig utsträckning kan ett alternativ vara att använda ett koncept likt ”Ljus som tjänst”. Dock är det inte säkert att det är lika kostnadseffektivt som en lokalt optimerad lösning.

Det finns hinder som försvårar energieffektivisering av industriell belysning. Litteraturstudien indikerar att de mest nämnda hindren är finansiella och informativa. Informativa hinder kan överkommas genom styrmedel såsom energikartläggningsstöd för små och medelstora företag samt energinätverksprogram. Om mervärden kan inkluderas i ett investeringsunderlag så skulle det kunna bidra till att finansiella hinder minskar. För att kunna göra detta behöver mervärdeskonceptet bli mer allmänt känt.

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.

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.

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biassed and underdispersed, meteorologists post-process the ensembles. This post-processing can successfully correct the biasses in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post-processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post-processing method and evaluate four possible strategies: only using the raw ensembles without post-processing, a one-step strategy where only the weather ensembles are post-processed, a one-step strategy where we only post-process the power ensembles and a two-step strategy where we post-process both the weather and power ensembles. Results show that post-processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness whilst only post-processing the weather ensembles does not necessarily lead to increased forecast performance.