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Alumet is a modular framework for "low-level" measurement. Thanks to an extensible plugin system, the framework allows to create measure-made measurement tools for a wide range of situations, local or distributed. With a generic model and interface, it can obtain, process and export any type of measurement, in the form of multi-dimensional timeseries. In particular, it can measure or estimate the energy consumption of software services. Alumet is written in Rust for optimal memory footprint and performance.

Correct some bugs, allows processing 3D datasets. Functional on Terradue Cloud Platform (http://docs.terradue.com/developer-sandbox/)

Sweet potato (SP) is a tuber crop which is grown in most parts of the world; the crop generates lots of waste throughout its supply chain. Currently the waste has little commercial utilization; the unutilized sweet potato root waste (SPW) is highly perishable and release methane as they decompose posing a serious problem of environmental pollution. Biogas potential of sweet potato has been tested as a mono substrate as well as co-substrate. Other studies have also reported that sweet potato has a complex molecular structure which is resistant to enzymatic digestion; however no studies have reported any pretreatment mechanism to alter the complex structure to ease anaerobic degradation. Consequently, the main objective of this research was to evaluate energy recovery through anaerobic digestion of thermochemically pretreated SPW. The following specific objectives were investigated: physicochemical characterization of SPW; thermochemical pretreatment of sweet potato waste; and determination of pretreatment factors combination which produces optimum biogas. The quality of biogas produced in terms of methane content as well as the resultant digestate as a biofertilizer were also analyzed. SPW was milled and then subjected to thermo-chemical pretreatment where sodium hydroxide of concentration (0.6g/L-3.5g/L), temperature (50 - 90 ) and pre-treatment time (30-120minutes) were varied. The experimental setup was based on central composite design with all the three factors at five levels and biogas yield as a response. The pretreated SPW was anaerobically digested under mesophilic condition over an incubation period of 22 days. The results from the study revealed that, thermochemical pretreatment on SPW improved both biogas yield and methane. The optimum conditions for biogas production were obtained at: NaOH concentration 2.9g/L, heating temperature 82 and treatment time 102 minutes. The pretreated SPW had superior results than the untreated SPW; the untreated SPW cumulatively yielded 28.23 ml/gSPW of biogas, while the ...

Globally, anthropogenic climate change has caused declines of seagrass ecosystems necessitating proactive restoration approaches which would ideally anticipate future conditions. In eastern Australia, environmental conditions in estuaries with meadows of the endangered seagrass Posidonia australis have warmed and acidified over the past decade and seagrass communities have declined in some estuaries. Securing these valuable habitats will require proactive conservation and restoration efforts that could be augmented with restoration focussed on boosting resilience to future change. Understanding patterns of selection and where seagrass meadows are adapted to particular environmental conditions is key for identifying optimal donor material for restoration. We use single nucleotide polymorphisms and genotype by environment analyses to identify candidate loci under putative selection to environmental stressors and assess genomic variation and allelic turnover along stressor gradients. The most important estuarine variables driving selection were associated with temperature, water turbidity and pH. We developed a preliminary 'donor registry' of pre-adapted Posidonia australis genotypes by mapping the distribution of alleles to visualise allelic composition of each sampled seagrass meadow. The registry could be used as a first step to select source material for future-proofing restoration projects however, manipulative experiments will be required to test that pre-adapted genotypes confer increased resistance to multiple environmental stressors. A total of 342 individual P. australis were initially genotyped with the DArTseq™ platform yielding a total of 11,382 SNP loci with a mean read depth of 7.07 and 20.73% missing data. Sequencing error was estimated by calculating the maximum proportion of allelic differences (bitwise distance) found between six pairs of technical replicates using bitwise.dist in the R package poppr which was used as a threshold. No sequencing errors were detected and technical replicates were then removed from the dataset. To enhance the quality of SNPs and to optimise the number of loci available for identification of candidate SNPs under potential selection, a data filtering strategy was employed using several functions in the R package dartR v.2.7.2. Data were filtered applying a locus call rate of 0.67, and individual call rate of 0.25 and a reproducibility threshold of 0.99. Read depth filter parameters were set at 2 to 50 and SNPs were thinned by setting the MAF to default (0.01). After filtering, a total of 3,277 SNP loci for 311 genotypes across the 13 populations were retained. Environmental data were sourced from various repositories, please see the published manuscript for source details

FLIDU (Floating Lidar Uncertainty) is a tool for the quantification of motion induced uncertainties of nacelle based lidar inflow measurments on floating offshore wind turbines (FOWT). The framework contains an analytical model of the wind field as well as the lidar measurements under consideration of FOWT dynamics. FOWT dynamics are modelled as harmonic oscillation and parameterized by their amplitude, frequency and mean value. Measurement uncertainty is derived by applying the GUM methodology on the equatuation of the analytical model.

Bayesian optimization software implementation for optimizing 1P + 2P IPC, controller integral gains and azimuth offset for both control loops. Paper title: Efficient tuning of Individual Pitch Control:\\A Bayesian Optimization Machine Learning approach Published at TORQUE 2020, Delft

Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit comprises R code (in R markdown) and html renders of these workbooks produced by Control REACT that: implement benchmark and advanced methods for probabilistic wind, solar and electricity demand forecasting verify and evaluate the skill of these forecasts A third output that demonstrate how these forecasts could be used in decision-making scenarios at NGESO may be added in a future realise but cannot be shared publicly at present. Accompanying data is held in a separate repository linked below. In order to re-run the code, the data and code must be arranged in directories as given in "Directory Structure.pdf" The methods implemented for regional and national wind, solar, and net-demand forecasting (day-ahead/daily update and 0-6h ahead 30 minute update) are implemented in R and annotated in detail in the Rmarkdown framework, which produces readable html renders of the descriptions and figures that can be read in a browser without the need to install or be familiar R. At the end of each document exact session information is provided listing R and package versions used. Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. Project details: Summary/background: The uncertainty that Control Room (CR) engineers must handle in their decision-making is growing rapidly due to increases in renewable and embedded generation. At the same time, the CR has seen a huge rise in the number of units involved in their balancing decisions (from 40 to over 1,000). It is inevitable then, that the costs of balancing the grid has also been rising and will continue to do so until an approach is adopted which allows CR engineers to effectively manage uncertainty. It is believed that if information about forecast uncertainty was presented in real-time to CR engineers, that this would provide opportunities for them to make more economic and secure balancing decisions. Outcomes form Workstream 2: The production of skillful and statistically robust forecasts of BMU wind power generation and embedded solar power, using best-in-class data-driven forecasting algorithms. Skillful and statistically robust forecasts of demand and embedded solar. An example approach for using probabilistic forecasting to support rescheduling and redispatch. An example approach for using probabilistic forecasting to support day ahead reserves recommendations. An example approach for using probabilistic forecasting to support margins analysis. Further infomation: https://smarter.energynetworks.org/projects/nia_ngso0032/ {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021"]}