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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Oxygen stress mitigation for microalgal biomass productivity improvement in outdoor raceway ponds

Dissolved oxygen (DO) concentrations at supersaturated levels are commonly observed in outdoor microalgae cultures due to photosynthesis during periods of rapid growth. Paradoxically, the photosynthetically generated oxygen is inhibitory and lowers biomass productivity, therefore, increasing the cost of microalgal biofuels. In this study, photosynthetic rates of Monoraphidium minutum 26B-AM and Scenedesmus obliquus UTEX393, two of the top DISCOVR strains, were compared at three DO levels, 0, 8, and 30 mg L -1 . Increasing DO from 0 to 30 mg L -1 caused a 36.6% and 26.1% reduction in photosynthetic rate for M. minutum 26B-AM and S. obliquus UTEX393, respectively. To improve the outdoor pond biomass productivity of each strain, three oxygen stress mitigation methods were tested. The first method raised the dissolved bicarbonate concentration by an order of magnitude to favor bicarbonate transport into the cell, increasing the CO 2 :O 2 ratio near RuBisCO, thus reducing photorespiration. Although this method increased the observed photosynthesis rate of M. minutum 26B-AM by 26% and S. obliquus UTEX393 by 13% in laboratory testing, it failed to improve biomass productivity under outdoor pond conditions for both strains. The second method added sodium sulfite as an oxygen scavenger to chemically lower the DO concentration in the culture. Chemical reduction of dissolved molecular oxygen did not significantly improve the biomass productivity of M. minutum 26B-AM; however, a 10% improvement in biomass productivity for S. obliquus UTEX393 was observed. The final method sparged cultures with air to strip oxygen out of the liquid medium. This did not improve the biomass productivity of M. minutum 26B-AM, likely due to unfavorable weather conditions during cold season cultivation; however, an improvement of 36% in biomass productivity was achieved for S. obliquus UTEX393 during the warm season cultivation. Of the three tested methods, air-sparging was the most effective and practical for improving outdoor biomass production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Genome sequence, phylogenetic analysis, and structure-based annotation reveal metabolic potential of Chlorella sp. SLA-04

Algae are a broad class of photosynthetic eukaryotes that are phylogenetically and physiologically diverse. Most of the phylogenetic diversity has been inferred from 18S rDNA sequencing since there are only a few complete genomes available in public databases. Here we use ultra-long-read Nanopore sequencing to determine a gapless, telomere-to-telomere complete genome sequence of Chlorella sp. SLA-04, previously described as Chlorella sorokiniana SLA-04. Chlorella sp. SLA-04 is a green alga that grows to high cell density in a wide variety of environments - high and neutral pH, high and low alkalinity, and high and low salinity. SLA-04's ability to grow in high pH and high alkalinity media without external CO 2 supply is favorable for large-scale algal biomass production. Phylogenetic analysis performed using ribosomal DNA and conserved protein sequences consistently reveal that Chlorella sp. SLA-04 forms a distinct lineage from other strains of Chlorella sorokiniana. We complement traditional genome annotation methods with high throughput structural predictions and demonstrate that this approach expands functional prediction of the SLA-04 proteome. Genomic analysis of the SLA-04 genome identifies the genes capable of utilizing TCA cycle intermediates to replenish cytosolic acetyl-CoA pools for lipid production. We also identify a complete metabolic pathway for sphingolipid anabolism that may allow SLA-04 to readily adapt to changing environmental conditions and facilitate robust cultivation in mass production systems. Altogether, this work clarifies the phylogeny of Chlorella sp. SLA-04 within Trebouxiophyceae and demonstrates how structural predictions can be used to improve annotation beyond sequencebased methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Reassessing methods to close the nuclear fuel cycle

This paper presents the major takeaways from studies conducted over several years that were focused on transitioning the U.S. nuclear infrastructure from the current once-through fuel cycle to one in which fuel is continuously recycled in fast reactors. Furthermore, these studies involved simulating and analyzing numerous example scenarios of fuel cycle transition with various assumptions on technology, policy, and material utilization strategies. Among the many findings, perhaps the most important is that under certain conditions, the use of high-assay low-enriched uranium to start up a fleet of fast reactors may be more favorable compared to using recycled Pu from thermal reactors since it is less constrained by other technologies and may even be more economical.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Predicting critical heat flux with uncertainty quantification and domain generalization using conditional variational autoencoders and deep neural networks

Deep generative models (DGMs) can generate synthetic data samples that closely resemble the original dataset, addressing data scarcity. In this work, we developed a conditional variational autoencoder (CVAE) to augment critical heat flux (CHF) data used for the 2006 Groeneveld lookup table. To compare with traditional methods, a fine-tuned deep neural network (DNN) regression model was evaluated on the same dataset. Both models achieved small mean absolute relative errors, with the CVAE showing more favorable results. Uncertainty quantification (UQ) was performed using repeated CVAE sampling and DNN ensembling. The DNN ensemble improved performance over the baseline, while the CVAE maintained consistent results with less variability and higher confidence. Both models achieved small errors inside and outside the training domain, with slightly larger errors outside. Altogether, the CVAE performed better than the DNN in predicting CHF and exhibited better uncertainty behavior.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Temperature-induced changes in the synthesis gas composition in a high-temperature H 2 O and CO 2 co-electrolysis system

Solid oxide electrolysis cell (SOEC) is an attractive renewable technology capable of producing synthesis gas (H 2 + CO) from H 2 O and CO 2 . An SOEC consists of a solid oxide electrolyte and two ceramic or metal-ceramic electrodes. H 2 O and CO 2 are electrolyzed at the cathode producing H 2 and CO, releasing O 2- ions, which travel through the electrolyte to the anode side and recombine to produce pure oxygen gas as the byproduct. In the present study, a La 0.7 Sr 0.2 Ni 0.2 Fe 0.8 O 3 cathode was used to co-electrolyze H 2 O and CO 2 at three different operating temperatures- 700 °C, 750 °C and 800 °C. Synthesis gas was produced at all three operating temperatures with 100% Faradaic efficiency. The composition of the syngas, however, was found to be a function of the operating temperature. In general, the H 2 /CO ratio in the syngas decreased with increased temperature. This was partially due to CO 2 electrolysis becoming more favorable compared to H 2 O electrolysis at higher temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Influence of thermal treatment on structure and catalytic performance of ceria-zirconia supported copper oxide (CuO x /Ce y Zr 1-y O 2 ) catalysts for CO oxidation

Copper oxide (CuO x ) supported on ceria-zirconia (Ce y Zr 1-y O 2 , y = 1.0, 0.5, 0.0) catalysts were investigated to elucidate the effects of thermal treatment on their physicochemical properties and catalytic performance in carbon monoxide (CO) oxidation. Here, the catalysts were synthesized via a one-pot chemical vapor deposition (OP-CVD) method at 700˚C and 900˚C with controlled Cu loading. Characterization techniques, including synchrotron X-ray diffraction (S-XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), inductively coupled plasma spectroscopy (ICP), and N 2 adsorption-desorption, were implemented to probe the crystalline structure, molecular and electronic structure, oxygen vacancies, specific surface area (SSA) and metal loading. CO oxidation was chosen as a model reaction to explore the structure-catalytic performance relationship. A ∼100% CO conversion was achieved at < 150˚C, particularly with the CuO x /CeO 2 catalyst calcined at 700˚C. In contrast, calcination at 900˚C caused a ∼90% decrease in SSA and a ∼24% increase in T 50 . Activity tests revealed that increasing ZrO 2 content lowered CO oxidation activity despite generating more defect sites. In-situ measurement of the 700 °C calcined samples revealed the presence of stable and unstable defects in CuO x /Ce 0.5 Zr 0.5 O 2 and CeO 2 respectively, which play a key role in the activity of the catalysts. The results highlight that catalytic performance is closely related to the SSA. Furthermore, an optimum calcination temperature favor significant oxygen vacancy formation with required CuO x -support interactions, enhancing redox properties and catalytic performance.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Alkylation of lignin-derived aromatic oxygenates with cyclic alcohols on acidic zeolites

Moderately strong BAS and spacious microporous environments (i.e., large-pore acidic zeolites HBEA and HY) are important criteria for efficient alkylation of phenols with cyclic alcohols and alkenes, while very strong BAS appear to be responsible for catalyst deactivation. BAS confined in HBEA and HY pores show substantially higher turnover frequencies compared to solids without molecularly sized pore constraints. HBEA favors the formation of mono-alkylates, while enhanced formation of di-alkylates is observed on HY. Dehydration of alcohols always dominates over the alkylation at the onset of the reaction. Carbenium ions, the direct electrophile for aromatic oxygenates, are produced mainly from the adsorption and protonation of olefins. The present study shows that for the alkylation of phenols and cyclic alcohols in apolar liquids, the alcohol concentration should be kept low to avoid the formation of unreactive surface dimers and mitigate their inhibitory effects on olefin adsorption and protonation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Selective hydrodeoxygenation of 5-hydroxymethylfurfural (HMF) to 2,5-dimethylfuran (DMF) over carbon supported copper catalysts using isopropyl alcohol as a hydrogen donor

Selective hydrodeoxygenation (HDO) of 5-hydroxymethylfurfural to 2,5-dimethylfuran is of great importance. Here, we reveal a simple pathway for green and efficient HDO using readily available copper with in-situ hydrogen generation. A highly dispersed Cu/PBSAC catalyst consisting of small metallic Cu 0 nanoparticles carries out isopropyl alcohol (IPA) dehydrogenation and subsequent HDO of HMF. Density functional theory calculations reveal that the dehydrogenation of IPA is more favorable on Cu(211) with a lower energy barrier of ~0.6 eV. This facet exists in a higher ratio on nanosized catalysts. Batch reactions using Cu/PBSAC at 190 °C exhibited 91.9% HMF conversion and 71.7% DMF selectivity in 6 hr, and > 96% DMF yield in 10 hr. The mechanical strength of the carbon support is ideal for continuous processing for increased productivity; we demonstrate a > 90% DMF yield at 1/WHSV of 2.4 hr. The process demonstrated here can be integrated with upstream HMF separation utilizing carbon adsorbents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Reaction chemistry of ethanol oligomerization to distillate-range molecules using low loading Cu/Mg x AlO y catalysts

We study ethanol oligomerization to higher alcohols and other oxygenates with a 0.3 wt. %Cu/Mg 2.9 AlO catalyst. This reaction involves more than 130 products in a complicated reaction network. The selectivity towards diesel fuel precursor compounds (hereafter ‘DFPC’) increased with conversion until reaching a plateau at an ethanol conversion of ~70 %. Alcohol selectivity was found to follow Schultz-Flory distribution at all studied conversions. Larger sized alcohols then are formed mostly by chain-growth mechanisms via surface reactions of adsorbed oligomers with ethanol-based monomers. Higher esters are formed from alcohols and aldehydes in a series reaction mechanism. Moreover, C 6+ ester and C 4+ ketones selectivities increase as conversion increases. We also found that C 4+ alcohols most likely undergo Guerbet coupling with the studied catalyst to form even higher alcohols once, and that the oxygen of these alcohols is active as a nucleophile, resulting in the selective formation of esters if the starting alcohol is branched. Finally, we performed several cofeed studies varying ethanol-to-H 2 inlet partial pressures, and adding acetaldehyde and ethyl acetate as cofeeds to ethanol at different concentrations. Here, we conclude from these experiments that acetaldehyde concentration controls reaction chemistry, with conditions favoring larger concentrations of the molecule promoting both alcohol coupling and ester formation, and conditions leading to lower concentrations of acetaldehyde resulting in higher alcohol selectivity at the expense of esters and higher aldehydes.

10 SYNTHETIC FUELS↗
DOE OSTI2023

Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions

The Sabatier principle is of fundamental importance to computational catalyst discovery, saving researchers time and expense by predicting catalytic activity in silico at scale. However, as polycrystalline and nanoscale catalysts increasingly dominate industry, computational screening tools must be adapted to these uses. In this work, we demonstrate the effectiveness of computational adsorption energy screening in nanocatalysis by comparing a multisite adsorption energy prediction workflow against a large experimental dataset of hydrogen evolution activities over bimetallic nanoparticles. Comparing 16 million hydrogen adsorption energy predictions with the hydrogen evolution activity of 5300 experiments across 84 monometallic and bimetallic systems, we discover that favorable adsorption energies are a necessary condition for experimental activity, but other factors often determine trends in practice. Finally, about half of the bimetallic search space can be excluded from experimental screens using hydrogen adsorption predictions, but this method may become significantly more powerful when combined with other screening tools.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Efficient and selective dual-pathway polyolefin hydro-conversion over unexpectedly bifunctional M/TiO 2 -anatase catalysts

The huge scale of plastic waste generation and its environmental consequences drive the demands for catalytic plastic upcycling processes. Here, we report efficient polypropylene (PP) hydro-conversion over Ru and Ni supported on a sol-gel anatase TiO 2 (TiO 2 -A-SG). A small number of Brønsted acid sites on TiO 2 -A-SG enables hydrocracking, improving efficiency, steering the selectivity towards high-valued C 4-20 , and allowing more isomerization, compared to Ru-based monofunctional hydrogenolysis catalysts. The relative contribution of hydrocracking increases with lower hydrogen partial pressure, more branched substrates, and lower Ru loading. Ni/TiO 2 -A-SG exhibits superior hydrocracking activity and selectivity to Ni on conventional Brønsted-acidic supports, particularly zeolites. Further, mechanistic studies shows fast isomerization and sequential ß-scissions caused by strong polymer-catalyst interaction and fast cracking with abundant 3 C. This favors C 4-12 formation and prevents secondary reactions. This work demonstrated a novel, highly efficient noble-metal-free catalyst for plastic waste upcycling, while advancing the mechanistic understanding of polyolefin hydro-conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

LaCeO x coupled N-doped graphene/Ru single-atoms as a binary-site catalyst for efficient hydrogen evolution based on hydrogen spillover

Based on the hydrogen spillover effect, versatile binary-site catalysts are promising for the alkaline hydrogen evolution reaction (HER). In which one site can adsorb and dissociate water, while the neighboring site is favorable for liberating hydrogen. Inspired by these possibilities, oxygen vacancy (O v )-rich LaCeO x coupled N-doped graphene/Ru single-atoms (LaCeO x @NGr/Ru 1 ) was synthesized as an efficient dual-site HER catalyst. It delivered an impressive low overpotential of 22 mV at a current density of 10mAcm –2 and a slight Tafel slope of 40 mV dec –1 in an alkaline medium, outstanding the advanced Ru-based catalysts. Moreover, this promising binary-component catalyst exhibited higher mass activity and longer lasting durability than commercial Pt/C catalyst (20 wt%). Finally, experimental and theoretical investigations provided insights into the HER mechanisms of LaCeO x @NGr/Ru 1 based on three indispensable steps: water adsorption and dissociation on O v -rich LaCeO x , diffusion of generated H* species towards Ce 3+ -N-Ru 1 bridges, and hydrogen evolution on Ru 1 sites.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Mechanisms and site requirements for NO and NH 3 oxidation on Cu/SSZ-13

Two series of Cu/SSZ-13 catalysts were synthesized via aqueous solution and solid-state ion exchange using SSZ-13 supports of varying Si/Al ratios. The isolated and multinuclear Cu content of these catalysts were determined by H 2 temperature programmed reduction (H 2 -TPR). Multinuclear Cu in these catalysts, including in situ Cu- dimers formed from ZCu II OH coupling and permanent CuO clusters, are active species for dry NO oxidation. NH 3 oxidation on these catalysts follows an internal SCR (i-SCR) mechanism, i.e., a portion of NH 3 is first oxidized to NO, then NO is selectively reduced by the remaining NH 3 to N 2 . NH 3 oxidation displays distinct kinetic behavior below ~300 °C and above ~400 °C. At low temperature the results indicate that NH 3 -solvated mobile Cu-ions are the active centers. CuO clusters, when present, also contribute to the low temperature activity by catalyzing NH 3 oxidation to NO. At high temperature, in situ Cu-dimers and CuO clusters catalyze NH 3 oxidation to NO, and isolated Cu-ions catalyze SCR to realize the cascade turnovers. For both NO and NH 3 oxidation, Cu-dimers balanced by framework charges of close proximity appear to be more active than Cu-dimers balanced by distant framework charges. However, the former Cu-dimers are less stable than the latter and tend to split into monomers in the presence of vicinal Brønsted acid sites. Via density functional theory (DFT) calculations, the i-SCR mechanism for low temperature NH 3 oxidation, i.e., the energetic favorability for the involvement of the NO intermediate, is justified. Furthermore, the DFT results also agree with experimental data that the formation of Cu-dimers from ZCu II OH dimerization is essential for NH 3 oxidation at high temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The role of catalyst acidity and microstructure on light olefin selectivity in polyethylene deconstruction in short contact time pulse Joule-heated reactors

The growing volume of plastics waste, compounded with a low recycling rate, has led to an alarming amount of plastics ending up in landfills or being incinerated. While pyrolysis offers a route for plastic waste deconstruction, its product distribution is often broad and poorly controlled due to unselective radical chemistry at high temperatures. We recently demonstrated that rapid pulse Joule-heated catalytic cracking over HZSM-5, combined with small fractions of steam, can achieve high selectivity (>80 %) toward C 2 -C 4 olefins, while significantly reducing coking compared to continuous Joule heating. Here, we investigate how acid catalyst properties, such as silica/alumina ratio, zeolite topology, and catalyst porosity, influence light olefin selectivity during polyethylene deconstruction via rapid pulse Joule heating. We demonstrate that silica-to-alumina ratios of ∼30 yield high light olefin selectivity, and small-pore zeolites favor light olefins at the expense of increased coke formation. To mitigate coking, we synthesize HZSM-5 nanosheets and hierarchical zeolites (MFI, FAU, and CHA). Furthermore, these catalysts achieve an ethylene selectivity of approximately 35 %, a twofold increase over prior catalytic pyrolysis. Additionally, co-feeding steam and incorporating hierarchical porosity reduce coke formation and enhance catalyst stability.

Catalytic cracking↗
DOE OSTI2020

Building thermal load prediction through shallow machine learning and deep learning

Building thermal load prediction informs the optimization of cooling plant and thermal energy storage. Physics-based prediction models of building thermal load are constrained by the model and input complexity. In this study, we developed 12 data-driven models (7 shallow learning, 2 deep learning, and 3 heuristic methods) to predict building thermal load and compared shallow machine learning and deep learning. The 12 prediction models were compared with the measured cooling demand. It was found XGBoost (Extreme Gradient Boost) and LSTM (Long Short Term Memory) provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day's data for prediction. Then, we discussed how the prediction horizon and input uncertainty would influence the load prediction accuracy. Major conclusions are twofold: first, LSTM performs well in short-term prediction (1 h ahead) but not in long term prediction (24 h ahead), because the sequential information becomes less relevant and accordingly not so useful when the prediction horizon is long. Second, the presence of weather forecast uncertainty deteriorates XGBoost's accuracy and favors LSTM, because the sequential information makes the model more robust to input uncertainty. Training the model with the uncertain rather than accurate weather data could enhance the model's robustness. Finally, our findings have two implications for practice. First, LSTM is recommended for short-term load prediction given that weather forecast uncertainty is unavoidable. Second, XGBoost is recommended for long term prediction, and the model should be trained with the presence of input uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A robust offering strategy for wind producers considering uncertainties of demand response and wind power

This paper proposes a risk-constrained decision-making approach for a wind power producer participating in the day-ahead market. In the developed model, a flexible demand response trading scheme between the wind power producer and different customers is employed. Through the proposed demand response mechanism, the wind power producer is able to trade demand response resource internally with different customers, and then trade energy externally with the market to increase the expected profit and the wind energy utilization. The uncertainties in the wind power and demand response are modeled by using the information gap decision theory approach from risk averse (robust) and risk-seeking (opportunistic) perspectives. The objective of the robust model is to maximize the robust level while satisfying the desired profit, whereas the opportunistic model aims to evaluate the possibility of achieving windfall profits with favorable uncertainties. The overall offering strategy problem is modeled as a bi-objective mixed integer nonlinear programming, which is linearized by proper techniques and solved efficiently by using the normal boundary intersection technique. In this work, simulation results show that utilizing demand response resource to mitigate wind power deviations can increase a wind power producer's profit and reduce potential risks. In addition, the results demonstrate that the proposed bi-objective optimization approach enables the wind power producer to select appropriate offering decisions with respect to uncertainties.

17 WIND ENERGY↗
DOE OSTI2020

Techno-economic prospects for producing Fischer-Tropsch jet fuel and electricity from lignite and woody biomass with CO 2 capture for EOR

This work explores the prospective techno-economic performance of facilities that produce low- and net-negative-carbon liquid transportation fuels and electricity with CO 2 capture for enhanced oil recovery. The lignite and biomass-to-jet fuel process is based on KBR’s TRIG gasifier, Rectisol (for sulfur removal and CO 2 capture), fixed-bed low temperature Fischer-Tropsch synthesis of liquid fuels, and Brayton/Rankine combined cycles to convert synthesis/refining off-gases and waste heat to electricity. This work leverages a recent, highly-detailed assessment of a prospective first-of-a-kind (FOAK) demonstration facility to develop highly detailed Aspen Plus process simulations for nine prospective N th -of-a-kind (NOAK) plant equipment configurations. Component-level capital costs from the FOAK study are scaled and adjusted to reflective prospective learning-by-doing to estimate capital costs for the NOAK designs. NOAK plant economic performance is found to be largely insensitive to variations in plant configurations and electricity output fraction, but biomass input fraction significantly affects profitability. Facilities that consume only carbon–neutral biomass, with no lignite co-feed, have significantly net-negative carbon emissions and the most favorable prospective economics when carbon emissions are priced. For these facilities, the crude oil price required for plant economic viability falls rapidly from $\$$100/bbl as carbon emission prices increase above $\$$120/tonne CO 2eq . In general, plants that co-fire lignite with biomass are less profitable (than 100% biomass plants) due to their higher net greenhouse gas emissions.

09 BIOMASS FUELS↗
DOE OSTI2021

Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

Residential electricity load profiles and their diversity have become increasingly important to realize the benefits of Smart or Transactive Energy Networks (TENs). An important element of TENs will be practical, accurate, and implementable residential load forecasting techniques. While there have been many approaches to short-term load forecasting, few have included forecasting for individual households, partly because the high volatility and idiosyncrasies present in individual household load data can pose significant challenges. In this study, we develop a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features (termed a CLSAF model) to improve short-term household electricity load forecasting accuracy by employing three strategies: autoregressive features selection, exogenous features selection, and a “default” state to avoid overfitting at times of high load volatility. We include aggregations of apartments to floor and building level, because utilities may favor transactive approaches that rely on aggregator models, e.g., a cluster of consumers as opposed to an individual. We demonstrate that the CLSAF model, by virtue of its enhanced feature representation and modest computational resources, can accomplish load forecasting in a multi-family residential building across three spatial granularities (individual apartment/household, floor, and building levels), with an accuracy improvement of up to 25% compared to a persistence model. We propose a data screening technique to characterize time-series electricity-load data. This technique is suitable for integration into a TEN ecosystem and allows one to estimate confidence levels of the load forecasts to optimize computational resources and the risks associated with uncertain forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗