• Energy Research
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The dataset accompanies the Nature Energy publication by Figgener et al. (2024), Multi-year field measurements of home storage systems and their use in capacity estimation, DOI 10.1038/s41560-024-01620-9. In addition, we use the dataset in Figgener et al. (2024), Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data, DOI 10.48550/arXiv.2411.08025 The ISEA / CARL of RWTH Aachen University measured 21 private home storage systems in Germany over up to eight years from 2015 to 2022. All these storage systems are combined with residential photovoltaic systems to increase self-consumption. The measured quantities published are system-level battery current, voltage, power, battery pack housing temperature, and room temperature. The sample rate is one second. The dataset consists of 106 system years, 14 billion data points, and 1,270 monthly files stored in 21 system folders. Use the data as follows:1. Download the data (Data_ID_01.zip to Data_ID_21.zip) and the belonging repository (Metadata_and_Code.zip) 2. Uncompress the files so that the uncompressed folders have the same name as the .zip files. 3. Copy all data folders in folder "Metadata_and_Code/00_Data/01_Operational_Data". Read and execute the file "StartUp_Read_and_Execute.m" and stay in this folder for any script you execute. In addition, a detailed description of the dataset and how to use it can be found in the supplementary information of the publication.

Due to climate change and current efforts to reduce emissions in the construction sector, this study evaluates and discusses the results of a comparative cradle-to-grave Life Cycle Assessment (LCA), with a main focus on Global Warming Potential for functionally equivalent carbon-reinforced concrete (CRC) and steel-reinforced concrete (SRC) façade panels for the first time. The novelty of this study is the focus on construction waste and, in particular, the worst-case application of non-recycled construction waste. The use of CRC requires a lower concrete thickness than SRC because the carbon fiber reinforcement does not corrode, in contrast to steel reinforcement. Façade panels of the same geometrical dimensions and structural performance were defined as functional units (FU). Assuming an End-of-Life (EoL) scenario of 50% landfill and 50% recycling, the Global Warming Potential (GWP, given in CO2 equivalent (CO2e)) of the CRC façade (411–496 kg CO2e) is shown to perform better than or equal to the SRC façade (492 kg CO2e). Changing the assumption of CRC to a worst-case scenario, going fully to landfill and not being recycled (single life cycle), turns the GWP results in favor of the SRC façade. Assuming a 50-year service life for the SRC façade panel and relativizing the emissions to the years, the more durable CRC façade performs much better. Finally, depending on the system boundary, the assumed EoL and lifetime, CRC can represent a lower-emission alternative to a functionally equivalent component made of SRC. The most important and “novel” result in this study, which also leads to future research opportunities, is that delicate adjustments (especially concerning EoL scenarios and expected service life) can lead to completely different recommendations for decision-makers. Only by combining the knowledge of LCA experts, structural engineers, and builders optimal decisions can be made regarding sustainable materials and building components.

This studypresentsan investigation of the annual cooling load in buildings by analyzingthe influenceof parameters of phase change materials(PCMs)integrated into the envelopes of the buildings. For the use cases, well-knownCases 600 and 650 of ASHRAE Standard 140were considered. Wemodified vertical walls of the use cases incorporating various PCM layers. The impact of variousfactors of PCM layers in four climates was assessed. These factors were the thickness, melting temperature, latent heat of fusion, density, specific heat capacity, and thermal conductivity. The results showed that the variation of the density, latent heat of fusion, and the thickness of PCMs had a high impact on the reduction ofthe annual cooling energy. However, the level of thickness, latent heat of fusion, and density stuck inthe maximum value, whereas the level of thermal conductivity and specific heat capacity stuckinthe minimum value. Generally, during the globaland multi-objective optimization problems, theseparameters may beexcludedfrom the variable settingsexcept for thickness whereby the penalty function can be set. The general thermodynamic pattern of the results concludes that buildings with lightweight envelopes require as much heat storage as possiblepreventing it from theflow ofheat to the surrounding.

Correction for ‘Sustainable electrochemical synthesis of dry formaldehyde from anhydrous methanol’ by Florian Schwarz et al., Green Chem., 2024, 26, 4645–4652, https://doi.org/10.1039/D3GC04978G.

AbstractAs a regenerative energy source, tidal energy can significantly contribute to greenhouse gas reduction, even though the potentially achievable energy output is lower than that of wind or solar energy. The decisive advantage of tidal turbines lies in the simply and reliably predictable energy output. However, their commercial use has so far been impeded by the fact that on the one hand complex mechanical systems are required to convert energy of tidal currents and on the other hand multi-axial loading conditions caused by turbulent ocean currents act on the turbine. For this reason, field tests on prototypes are an essential part of the development strategy to ensure operational reliability. However, in-field tests do not allow for accelerated lifetime testing, so that test bench experiments are becoming an increasingly important alternative. Today, established procedures for testing the turbines main bearings and gearing system are already available, both for setting up the required test configuration and for determining the corresponding test loads. However, the use of advanced calculation methods, such as the finite element method for stress calculation, requires a deep understanding of the examined components and hinders the transfer of the approaches to other components.To simplify the process of test loads determination, a general methodology is presented, which relies exclusively on standardized empirical calculation rules. Doing this, fatigue equivalent loads can be determined for any component in a simple process. It was shown that the achieved reduction in complexity opens further potential for test acceleration, since several components can be tested simultaneously.

Abstract A thermogravimetric analyzer (TGA), a fluidized bed reactor (FBR) and a drop tube reactor (DTR) are used to study the effect of reactor type, heating rate and temperature on the pyrolysis of pulverized walnut shell particles in N2 and in CO2. These setups cover a temperature range of 400–1300 K with heating rates of 10−1 to 105 K s−1. The single first-order model in combination with an Arrhenius approach is used to describe the pyrolysis reaction. Derived activation energies for all setups show similar values ( E a , TGA = 71.96 kJ mol−1, E a , FBR = 68.60 kJ mol−1 and E a , DTR = 60.83 kJ mol−1), while an increase in the reactor temperature tend to lower the activation energy. Pyrolysis gas compositions in FBR and DTR reveal consistent trends towards lower H2O and higher CO contents with increasing reactor temperature. To evaluate the impact of CO2 on the solid conversion, TGA measurements in CO2 are used to determine gasification kinetics ( E a , g = 214.1 kJ mol−1, A g = 71.96 s−1). CFD simulations using these kinetics in CO2 drop tube experiments let assume that the changed thermophysical properties of the gas and not the gasification reaction lead to the observed stronger conversion in CO2 compared to N2.

Abstract The integration of renewable energy sources into building energy systems and the progressive coupling between the thermal and electrical domains makes the analysis of these systems increasingly complex. At the same time, however, more and more building monitoring data is being collected. The manual evaluation of this data is time-consuming and requires expert knowledge. Hence, there is a strong need for tools that enable the automatic knowledge extraction from these huge data sets to support system integrators and favor the development of smart energy services, e.g., predictive maintenance. One crucial step in knowledge extraction is the detection of change points and hidden states in measurements. In this work, we present a tool for automated detection of hidden operation modes based on multivariate time series data deploying motif-aware state assignment (MASA). The tool is evaluated utilizing measurements of a heat pump and compared to two baseline algorithms, namely k-Means and k-Medoids. MASA performs particularly well on noisy data, where it shows only a small deviation in the number of detected change points compared to the ground truth with up to 77% accuracy. Furthermore, it almost always outperforms the baseline algorithms, which in turn require extensive preprocessing.