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El proyecto REVen “Rehabilitación energética de viviendas sociales, aplicando productos innovadores de ventana con marcado CE” (BIA2014-56650-JIN), tiene como objetivo realizar un análisis integral del impacto de la ventana en los aspectos relativos a eficiencia energética y calidad ambiental. Para caracterizar los flujos de energía y las condiciones ambientales internas se ha construido el Laboratorio REVen. Este artículo describe la construcción y la monitorización de este laboratorio analizando los datos de su primer año de funcionamiento. Los resultados permiten afirmar que se logra una mejora significativa del confort térmico obteniendo un ahorro de energía anual del 25 %.

AbstractUnderstanding the mechanisms controlling forest carbon accumulation is crucial for predicting and mitigating future climate change. Yet, it remains unclear whether the dominance of ectomycorrhizal (EcM) trees influences the carbon accumulation of entire forests. In this study, we analyzed forest inventory data from over 4000 forest plots across Northeast China. We find that EcM tree dominance consistently exerts a positive effect on tree, soil, and forest carbon stocks. Moreover, we observe that these positive effects are more pronounced during unfavorable climate conditions, at lower tree species richness, and during early successional stages. This underscores the potential of increasing the dominance of native EcM tree species not only to enhance carbon stocks but also to bolster resilience against climate change in high-latitude forests. Here we show that forest managers can make informed decisions to optimize carbon accumulation by considering various factors such as mycorrhizal types, climate, successional stages, and species richness.

This deliverable includes the methodology in EnergyPROSPECTS for an in-depth study of energy citizenship. It features the criteria used for selecting the cases for indepth study, the list of cases selected for in-depth study as well as key research foci and empirical research questions.

Studying the influence of the demand response and dynamic characteristics of the battery energy storage on the configuration and optimal operation of battery energy storage system (BESS) in the Wind-Photovoltaic (PV)-Energy Storage (ES) hybrid microgrid. A demand response model that is based on electricity price elasticity is established based on the time-of-use price. Take the capital-operating cost and direct economic benefit of the BESS and the loss of abandoned photovoltaic and wind power as the optimization objective, an optimal configuration method that considers the dynamic characteristics of the BESS and the maximum absorption of photovoltaic and wind power is proposed while using particle swarm optimization to solve. The results show that the configuration results considering the demand side response of the microgrid BESS can obtain better economy and reduce the storage capacity requirement, and the result shows that the efficiency of BESS relates to the load of the system, the distributed generation (DG) characteristics, and the dynamic characteristics of BESS. Meanwhile, the capacity and power of the energy storage configuration increase as the DG permeability increases due to the reverse load characteristic of the wind power.

Considering recent environmental regulations, the need to adapt domestic biomass combustion systems to models that generate less emissions has gained relative importance. The present research proposes an analysis of the bed cooling effects on emission patterns, specifically focusing on the concentration, typology and morphological aspects of the released particles. The study was carried out by comparing the behaviour of a small-scale pilot plant with air stratification, with and without bed cooling. The results revealed an optimal behaviour of the facility with distributions of 30% primary-70% secondary air, accompanied by a significant decrease in emissions due to the reduction in the operating temperatures. More than 75% of the particles were retained in the bed on the cooled surfaces due to the effect of the prominent temperature gradient that was produced. Among the types of emitted particles (mostly with sizes below 0.1 μm), the presence of partial biomass degradation remnants was observed, representing three-quarters of the total collected matter. To a lesser extent, the presence of carbonaceous agglomerates was detected and usually in very compact clusters; however, in cases of high primary air supply, large amounts of immature soot were observed. Agencia Estatal de Investigación | Ref. RTI2018-100765-B-I00

Abstract Numerical simulations with detailed homogeneous (gas phase) and heterogeneous (catalytic) chemistries of premixed hydrogen–oxygen mixture were performed inside a rectangular micro-combustor. The effects of catalytic walls on the homogeneous combustion were investigated via varying catalyst segment layouts and sizes. The interactions between heterogeneous and homogeneous reactions were discussed. It was shown that the heterogeneous reactions become weak when the catalyst disposition is shifted toward the outlet along the streamwise. The homogeneous combustion also becomes obviously weakened downstream behind the catalyst segmentation, except the exit. Moreover, with increasing catalyst segment size, the hydrogen conversion ratio increases and the mean outlet temperature reduces. The competition of the fresh fuel between the heterogeneous and homogeneous reactions result in the inhibition of heterogeneous reactions on homogeneous combustion. In summary, the heterogeneous reactions can obviously improve the combustion efficiency in a micro catalytic combustor. As expected, the effects of catalytic walls were more pronounced for the micro combustor systems.

Different species within the same community may exhibit distinct phenological responses to climate change, so it is necessary to study species differences in the green-up date among abundant species within a wide area, and a suitable phenology model should be introduced to explain the associated climate-driven mechanism. Although various models have been developed, very few studies have aimed to compare their efficiency and robustness, and the relative contributions of climate driving factors have not been sufficiently examined. We analyzed phenology data for 12 species across 17 stations in Inner Mongolia and found that essential spatiotemporal and interspecies differences existed in the green-up date. Five process-based models were established for each species and their performance was comprehensively evaluated. The two-phase models (sequential model, parallel model, unified model and unified model combined with precipitation driving) generally performed better than the one-phase model (thermal time model), and the model considering precipitation performed the best, which indicates that it is necessary to introduce the chilling effect and precipitation driving effect to improve the model accuracy in arid environments. We proposed a method to estimate the contribution rates of various climate driving factors, and significant differences in the relative demand for the various climate driving factors among different species were clearly revealed. The results indicated that for natural vegetation in Inner Mongolia, the need for the chilling and temperature driving is relatively high, and the precipitation driving is very important for herbaceous vegetation, which leads to considerable spatial and interspecies differences in green-up date. We demonstrated the feasibility of quantitatively evaluating the contributions of different climate driving factors with a process-based model, and the contradiction in phenological changes among different studies may eventually be clarified.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.