Fossil fuels serve a substantial fraction of global energy demand, and one major energy consumer is the global building stock. In this work, we propose a framework to guide practitioners intending to ...develop advanced predictive building control strategies. The framework provides the means to enhance legacy and modernized buildings regarding energy efficiency by integrating their available instrumentation into a data-driven predictive cyber-physical system. For this, the framework fuses two highly relevant approaches and embeds these into the building context: the generic model-based design methodology for cyber-physical systems and the cross-industry standard process for data mining. A Spanish school's heating system serves to validate the approach. Two different data-driven approaches to prediction and optimization are used to demonstrate the methodological flexibility: (i) a combination of Bayesian regularized neural networks with genetic algorithm based optimization, and (ii) a reinforcement learning based control logic using fitted Q-iteration are both successfully applied. Experiments lasting a total of 43 school days in winter 2015/2016 achieved positive effects on weather-normalized energy consumption and thermal comfort in day-to-day operation. A first experiment targeting comfort levels comparable to the reference period lowered consumption by one-third. Two additional experiments raised average indoor temperatures by 2K. The better of these two experiments only consumed 5% more energy than the reference period. The prolonged experimentation period demonstrates the cyber-physical system-based approach's suitability for improving building stock energy efficiency by developing and deploying predictive control strategies within routine operation of typical legacy buildings.
Integrated operation of distribution grids for multiple energy carriers promises hitherto unused synergies in terms of efficient generation, storage, and consumption. A major obstacle to the ...investment in such systems is their increased complexity, as conventional tools and methods were not designed to capture all relevant technical and economic aspects of hybrid grids. To address this obstacle, this work proposes a methodology to systematically assess multi-carrier energy grids under a holistic scope. By adopting a simulation-based approach that relies on detailed technical and economic models, an efficient and precise evaluation of both short-term (operational) and long-term (strategic) aspects is supported. The methodology enables the assessment of system configurations, control strategies, business models, and regulatory conditions in one coherent approach. As a proof-of-concept, the new methodology is applied to a real-world use case of a hybrid thermal-electrical distribution grid in a central European city. The results are comprehensively discussed to showcase how the various aspects of hybrid energy systems are addressed. The outcomes also demonstrate how this methodology aids the involved stakeholders in understanding the associated risks and potentials, paving the way for early adopters to realize multi-carrier energy distribution grids.
•A holistic methodology to study multi-carrier energy systems is presented.•Operational technical and strategic economic perspectives are considered.•Established tools for different domains are coupled in a co-simulation framework.•Multiple market participants and opposing objectives are taken into account.•The methodology is demonstrated by a comprehensive study in a central European city.
We present two case studies on energy grid hybridization, where the distribution networks of multiple energy sectors are more tightly coupled together to increase their flexibility via mutual ...transfer of energy. The hybridization approaches were developed in cooperation with the local stakeholder in a northern European city, comprising of a short-term setup with a low adoption barrier as well as a long-term scenario with more involved grid coupling using more efficient devices. For a range of coupling device configurations, device locations, control algorithms, and assumptions on utility prices and energy demand, we investigate the influence of the hybridization on the energy mix, CO 2 emissions, and energy costs. The studies have been conducted using a co-simulation toolchain developed by the European Project OrPHEuS specifically for fine-grained technical simulation of multi-carrier grids. Our results confirm that the hybrid grid approach is an effective means to increase the share of renewable energies and reduce operational costs. It also turns out that precise forecasts of energy demand and utility prices are essential for appropriate dimensioning of the coupling points.
Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized evaluation protocols and ...rigorous comparison across TKG models are available, but the importance of simple baselines is often neglected in the evaluation, which prevents researchers from discerning actual and fictitious progress. We propose to close this gap by designing an intuitive baseline for TKG Forecasting based on predicting recurring facts. Compared to most TKG models, it requires little hyperparameter tuning and no iterative training. Further, it can help to identify failure modes in existing approaches. The empirical findings are quite unexpected: compared to 11 methods on five datasets, our baseline ranks first or third in three of them, painting a radically different picture of the predictive quality of the state of the art.
PDF
