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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Challenges in determining b value in the Northwest Geysers

Past analyses of the Gutenberg-Richter b-value in the Geysers and other geothermal settings have revealed a deviation from the assumed linear relationship in log space between magnitude and the number of earthquakes. In this study of the Northwest Geysers, we found a gently-sloping discontinuity in the b-value curve. This is especially apparent when comparing the least-squares fit (LSQ) of the curve to the fit obtained by the maximum likelihood estimation (MLE), a widely-respected method of analyzing magnitude-frequency relationships. This study will describe the assumptions made when using each of these two methods and will also explore how they can be used in conjunction to investigate the characteristics of the observed b-value curve. To understand whether slope-fit differences in the LSQR and MLE methods is due to physical properties of the system or due to artifacts from errors in sampling, it is extremely important to consider the catalog completeness, magnitude bin size, number of events, and differences in source mechanisms for the events comprising the study volume. This work will hopefully lead to informative interpretations of frequency-magnitude curves for the Northwest Geysers, a geothermal area of ongoing high-volume coldwater injection and steam production. Through this statistical investigation of the catalog contents, we hope to better understand the dominant source mechanisms and the role of injected fluids in the creation of seismic clustering around nearly 60 wells of varying depths and injection volumes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

The phase boundary for the chiral transition in (2+1)-flavor QCD at small values of the chemical potential

We determine the chiral phase transition line in (2+1)-flavor QCD for small values of the light quark chemical potential. We show that for small values of the chemical potential the curvature of the phase transition line can be deduced from an analysis of scaling properties of the chiral condensate and its susceptibilities. To do so we extend earlier studies of the magnetic equation of state in (2+1)-flavor QCD to finer lattice spacings, aT = 1/8. We use these universal scaling properties of the chiral order parameter to extract the curvature of the transition line at two values of the cutoff, aT = 1/4 and 1/8. We find that cutoff effects are small for the curvature parameter and determine the transition line in the chiral limit to leading order in the light quark chemical potential. We obtain T{sub c}({micro}{sub q})/T{sub c}(0)=1-0.059(2)(4)({micro}{sub q}/T){sup 2+}O({micro}{sub q}{sup 4}).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Representation of the Solar Capacity Value in the ReEDS Capacity Expansion Model: Preprint

An important emerging issue is the estimation of renewables' contributions to reliably meeting system demand, or their capacity value. While the capacity value of thermal generation can be estimated easily, assessment of wind and solar requires a more nuanced approach due to resource variability. Reliability-based methods, particularly, effective load-carrying capacity (ELCC), are considered to be the most robust techniques for addressing this resource variability. The Regional Energy Deployment System (ReEDS) capacity expansion model and other long-term electricity capacity planning models require an approach to estimating CV for generalized PV and system configurations with low computational and data requirements. In this paper we validate treatment of solar photovoltaic (PV) capacity value by ReEDS capacity expansion model by comparing model results to literature for a range of energy penetration levels. Results from the ReEDS model are found to compare well with both comparisons--despite not being resolved at an hourly scale.

14 SOLAR ENERGY↗
DOE OSTI2014

Electrorecycling of Critical and Value Metals from Mobile Electronics

Mobile electronic devices such as smart phones and tablets are a significant source of valuable metals that should be recycled. Each year over a billion devices are sold world-wide and the average life is only a couple years. Value metals in phones are gold, palladium, silver, copper, cobalt and nickel. Devices now contain increasing amounts of rare earth elements (REE). In recent years the supply chain for REE has moved almost exclusively to China. They are contained in displays, speakers and vibrators within the devices. By US Department of Energy (DOE) classification, specific REEs (Nd, Dy, Eu, Tb and Y) are considered critical while others (Ce, La and Pr) are deemed near critical. Effective recycling schemes should include the recovery of these critical materials. By including more value materials in a recovery scheme, more value can be obtained by product diversification and less waste metals remains to be disposed of. REEs are mined as a group such that when specific elements become critical significantly more ore must be processed to capture the dilute but valuable critical elements. Targeted recycling of items containing the more of the less available critical materials could address their future criticality. This presentation will describe work in developing aqueous electrochemistry-based schemes for recycling metals from scrap mobile electronics. The electrorecycling process generates oxidizing agents at an anode while reducing dissolved metals at the cathode. E vs pH diagrams and metals dissolution experiments are used to assess effectiveness of various solution chemistries. Although several schemes were envisioned, a two stages process has been the focus of work: 1) initial dissolution of Cu, Sn, Ag and magnet materials using Fe+3 generated in acidic sulfate and 2) final dissolution of Pd and Au using Cl2 generated in an HCl solution. Experiments were performed using simulated metal mixtures. Both Cu and Ag were recovered at ~ 97% using Fe+3 while leaving Au and Ag intact. REE were extracted from the dissolved mixture using conventional methods. A discussion of future research directions will be discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Electrorecycling of Critical and Value Metals from Mobile Electronics

Mobile electronic devices such as smart phones and tablets are a significant source of valuable metals that should be recycled. Each year over a billion devices are sold world-wide and the average life is only a couple years. Value metals in phones are gold, palladium, silver, copper, cobalt and nickel. Devices now contain increasing amounts of rare earth elements (REE). In recent years the supply chain for REE has moved almost exclusively to China. They are contained in displays, speakers and vibrators within the devices. By US Department of Energy (DOE) classification, specific REEs (Nd, Dy, Eu, Tb and Y) are considered critical while others (Ce, La and Pr) are deemed near critical. Effective recycling schemes should include the recovery of these critical materials. By including more value materials in a recovery scheme, more value can be obtained by product diversification and less waste metals remains to be disposed of. REEs are mined as a group such that when specific elements become critical significantly more ore must be processed to capture the dilute but valuable critical elements. Targeted recycling of items containing the more of the less available critical materials could address their future criticality. This presentation will describe work in developing aqueous electrochemistry-based schemes for recycling metals from scrap mobile electronics. The electrorecycling process generates oxidizing agents at an anode while reducing dissolved metals at the cathode. E vs pH diagrams and metals dissolution experiments are used to assess effectiveness of various solution chemistries. Although several schemes were envisioned, a two stages process has been the focus of work: 1) initial dissolution of Cu, Sn, Ag and magnet materials using Fe+3 generated in acidic sulfate and 2) final dissolution of Pd and Au using Cl2 generated in an HCl solution. Experiments were performed using simulated metal mixtures. Both Cu and Ag were recovered at ~ 97% using Fe+3 while leaving Au and Ag intact. REE were extracted from the dissolved mixture using conventional methods. A discussion of future research directions will be discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Incomplete Data in Smart Grid: Treatment of Values in Electric Vehicle Charging Data

In this paper, five imputation methods namely Constant (zero), Mean, Median, Maximum Likelihood, and Multiple Imputation methods have been applied to compensate for missing values in Electric Vehicle (EV) charging data. The outcome of each of these methods have been used as the input to a prediction algorithm to forecast the EV load in the next 24 hours at each individual outlet. The data is real world data at the outlet level from the UCLA campus parking lots. Given the sparsity of the data, both Median and Constant (=zero) imputations improved the prediction results. Since in most missing value cases in our database, all values of that instance are missing, the multivariate imputation methods did not improve the results significantly compared to univariate approaches.

Majipour, Mostafa↗
DOE OSTI2016

Modeling the Value of Integrated Canadian and U.S. Power Sector Expansion

The United States and Canada power systems are not isolated. Cross-border transmission and coordination of system operation create an interconnected power system, which results in combined imports and exports of electricity of greater than 70 TWh per year [1]. Currently, over 5 GW of new international transmission lines are in various stages of permitting and development. These lines may enable greater integration and coordination of the U.S. and Canada systems, which can in turn reduce challenges associated with integration of high penetrations of variable renewables. Furthermore, low-cost Canadian resources, such as wind and hydro, could contribute to compliance with the EPA's recently released Clean Power Plan. Improving integration and coordination internationally will reduce the costs of accessing these resources. This analysis work build on previous work by Ibanez and Zinaman [2]. In this work we seek to better understand the value of additional interconnection between the U.S. and Canadian power systems. Specifically, we quantify the value of additional interconnection and coordination within the Canadian-US integrated power system under scenarios in which large reductions (>80%) in power sector CO2 emissions are achieved. We explore how the ability to add additional cross-border transmission impacts capacity investment, the generation mix, system costs, and the ability of the system to integrate variable renewable energy into the power system. This analysis uses the Regional Energy Deployment System (ReEDS) capacity expansion model [3], [4] to quantify the value of the integrated power system expansion of the United States and Canada. ReEDS is an optimization model that assesses the deployment and operation (including transmission) of the electricity sector of the contiguous United States and Canadian provinces from 2016 through 2050. It has the ability to model the integration of renewable energy technologies into the grid. ReEDS captures renewable energy resources through the use of 356 individual resource regions and 134 balancing areas across the U.S. and is able to handle renewable energy issues such as variability in wind and solar output, transmission costs and constraints, and ancillary services requirements.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

8760-Based Method for Representing Variable Generation Capacity Value in Capacity Expansion Models: Preprint

Capacity expansion models (CEMs) are widely used to evaluate the least-cost portfolio of electricity generators, transmission, and storage needed to reliably serve demand over the evolution of many years or decades. Various CEM formulations are used to evaluate systems ranging in scale from states or utility service territories to national or multi-national systems. CEMs can be computationally complex, and to achieve acceptable solve times, key parameters are often estimated using simplified methods. In this paper, we focus on two of these key parameters associated with the integration of variable generation (VG) resources: capacity value and curtailment. We first discuss common modeling simplifications used in CEMs to estimate capacity value and curtailment, many of which are based on a representative subset of hours that can miss important tail events or which require assumptions about the load and resource distributions that may not match actual distributions. We then present an alternate approach that captures key elements of chronological operation over all hours of the year without the computationally intensive economic dispatch optimization typically employed within more detailed operational models. The updated methodology characterizes the (1) contribution of VG to system capacity during high load and net load hours, (2) the curtailment level of VG, and (3) the potential reductions in curtailments enabled through deployment of storage and more flexible operation of select thermal generators. We apply this alternate methodology to an existing CEM, the Regional Energy Deployment System (ReEDS). Results demonstrate that this alternate approach provides more accurate estimates of capacity value and curtailments by explicitly capturing system interactions across all hours of the year. This approach could be applied more broadly to CEMs at many different scales where hourly resource and load data is available, greatly improving the representation of challenges associate with integration of variable generation resources.

14 SOLAR ENERGY↗
DOE OSTI2019

Applying Value at Risk Analysis towards Energy Efficiency Investments

Value at Risk (VaR) is a measure of risk for potential losses within a portfolio of investments. Investments in energy efficiency in a manufacturing environment compete in terms of payback period, ease of investment, and transaction costs with other investments (production, safety, engineering, etc.). Because manufacturers have limited amounts of capital to allocate, investments in productivity tend to be prioritized over energy efficiency projects that offer similar paybacks. Value at Risk (VaR) can be used to quantify how risk-weighted expenditures change over time. Energy efficiency investments are often believed to offer relatively stable payoffs compared to changes in production and other assets, particularly when evaluated using methodologies such as simple payback. Incorporating methodologies such as VaR can provide a stronger business case for energy efficiency investments by quantifying value beyond what is captured in other Return on Investment calculations.In this paper we outline a methodology for applying VaR in the context of the variety of investments made by manufacturing organizations. Additionally, we profile the stability of energy efficiency (EE) and renewable energy (RE) investments compared to other types of investments made in manufacturing. By applying VaR to these investments, energy managers can better translate the financial benefits of energy-related capital projects in a way that is more compelling to Corporate Finance and Treasury departments. This common language also allows Corporate Finance and Treasury departments to support large, strategic deployments of capital for such projects based on a targeted objective – the management of financial risk for the organization’s shareholders.

Levine, Eli↗
DOE OSTI2019

Electrolysis' Potential Value for Supporting the Electrical Grid

The H2@Scale concept includes a value proposition for electrolysis that benefits both hydrogen markets and the electricity system. Electrolysis has the potential to be an alternative low-cost hydrogen source by using low-price electricity that may be available for only a limited number of hours annually. It also has the potential to provide value to the grid through use of that low-cost electricity. By doing so, electrolysis can create a price floor which could support additional investment in low-cost, dispatch-constrained electricity (LDE) generation such as photovoltaic solar, wind, and nuclear. The rapid-response characteristics of low-temperature electrolysis enables it to also provide ancillary services to the grid such as operating reserves and frequency control. We evaluate the economic performance of water electrolysis in a selection of electricity markets - both current and potential future markets. To perform that evaluation, we collect locational marginal prices (LMPs) in each market, calculate levelized hydrogen costs, and compare them to cost estimates of producing hydrogen via steam methane reforming of natural gas. Those calculations also include electolyzer capital and operating costs. We perform the calculations across a range of capacity factors to identify the minimum levelized hydrogen cost in that range. We discuss the implications of that capacity factor for each market analyzed. For a couple of the analyzed electricity markets, we include the potential value of providing ancillary services in the calculation and discuss those impacts. LDE generation has been shown to suppress electricity prices because it generates electricity when the resource is available (i.e., the sun is shining, wind is blowing, nuclear heat is being produced) and the generation cannot be reduced without an economic penalty. Price suppression is likely to reduce investment in those technologies due to reductions in projected income of each marginal unit. If electrolyzers are willing to pay for the LDE, it could provide a price floor for electricity. We analyze how setting a price for LDE could impact the electricity generation fleet, the availability of LDE, and the potential levelized costs of producing hydrogen using that LDE. Among other results, we will present a set of supply curves showing potential production costs and availability for hydrogen produced from LDE in a future with high penetrations of renewable electricity on the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Fractionation of Lignocellulosic Biomass into High-Value Products - CRADA Report

Idaho National Laboratory (INL) has developed a process for adding value in the biomass feedstock supply chain. The key aspects of this process include: (1) anaerobic and chemical treatment of biomass during storage, (2) fractionation of biomass into conversion-ready feedstock and high-value coproducts, and (3) high-moisture pelleting to increase the bulk density and stabilize the feedstock. The purpose of the chemical treatment is to overcome the recalcitrant nature of the feedstock to improve the biochemical conversion process of breaking down the biomass into sugars. To maximize the utilization of the biomass, the feedstock can be fractionated into hemicellulose and lignin rich fractions. Finally, to increase the materials overall energy density, produce a shelf-stable material, and to facilitate transport, the feedstock is densified in a pelleting process. The first two steps of the processes have been verified at bench scale showing an improvement in theoretical glucose yield from 23.1% to 44.5% and an improvement in theoretical xylose yield from 8.9% to 23.1% from ground corn stover. It is hypothesized that densification will further increase the carbohydrate yield in the sample. The goal of this project was to evaluate the technical feasibility and process economics of the above process and to provide preliminary data to Golden Leaf Energy, Inc to access its potential commercialization for converting biomass to ethanol and renewable chemicals. Corn stover (CS), the largest available agricultural residue, and Chinese tallow (CT), an invasive wood species with no current commercial value, were chosen as feedstocks for this project.

09 BIOMASS FUELS↗
DOE OSTI2013

World Record Earned Value Management System Certification for Cleanup of the East Tennessee Technology Park, Oak Ridge, Tennessee, USA - 13181

On projects that require Earned Value Management (EVMS) Certification, it is critical to quickly prepare for and then successfully obtain certification. This is especially true for government contracts. Projects that do poorly during the review are subject to financial penalties to their company and they lose creditability with their customer creating problems with the project at the outset. At East Tennessee Technology Park (ETTP), we began preparing for Department of Energy (DOE) certification early during proposal development. Once the contract was awarded, while still in transition phase from the previous contractor to our new company, we immediately began reviewing the project controls systems that were in place on the project and determined if any replacements needed to be made immediately. The ETTP contract required the scheduling software to be upgraded to Primavera P6 and we determined that no other software changes would be done prior to certification. Next, preparation of the Project Controls System Description (PCSD) and associated procedures began using corporate standards as related to the project controls systems. During the transition phase, development was started on the Performance Measurement Baseline which is the resource loaded schedule used to measure our performance on the project and which is critical to good Earned Value Management of the project. Early on, and throughout the baseline review, there was positive feedback from the Department of Energy that the quality of the new baseline was good. Having this superior baseline also contributed to our success in EVMS certification. The combined companies of URS and CH2M Hill had recent experience with certifications at other Department of Energy sites and we were able to capitalize on that knowledge and experience. Generic PCSD and procedures consistent with our co-operations approach to Earned Value Management were available to us and were easily tailorable to the specifics of our contract and site. We also had corporate EVMS experts available to us so as to draw upon their recent certification experiences with lessons learned. This knowledge was especially helpful for training of personnel that were involved in the certification which included Project Controls, Project Management and Control Account Managers. We were also able to bring in these corporate experts to assist with our training efforts. To assure our readiness for the review, we conducted a 'White Hat' review. The 'White Hat' team consisted of corporate experts in EVMS along with an industry expert in EVMS from Humphrey and Associates. This review identified early any weaknesses that we had so corrections could be enacted prior to the EVMS Certification Readiness Review. It also helped give the evaluators confidence that we had done proper due diligence prior to their arrival. Also critical to our success, was early communication with our evaluators. It is important to start the communications early to ensure you understand the expectations of the certification team and the process that will be used during the certification. Communication through the entire process is critical to understand expectations and issues along the way. Very important to the overall process was management commitment, support and reinforcement. Management made sure that all personnel involved knew the importance and made preparations a priority. This was noted as a key strength by the evaluators during the out-brief. As a result of our preparation, our review yielded one Corrective Action Report (CAR) and two Continuous Improvement Opportunities (CIOs). The Certification team in their out-brief explained that this was the lowest number of CARs and CIOs in the history of EVMS certifications in the DOE Complex. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Extending Power of Nature from Binary Problems to Real-Valued Graph Learning in Real World

Nature performs complex computations constantly at clearly lower cost and higher performance than digital computers. It is crucial to understand how to harness the unique computational power of nature in Machine Learning (ML). In the past decade, besides the development of Neural Networks (NNs), the community has also relentlessly explored nature-powered ML paradigms. Although most of them are still predominantly theoretical, a new practical paradigm enabled by the recent advent of CMOS-compatible room-temperature nature-based computers has emerged. By harnessing the nature's power of entropy increase, this paradigm can solve binary learning problems delivering immense speedup and energy savings compared with NNs, while maintaining comparable accuracy. Regrettably, its values to the real world are highly constrained by its binary nature. A clear pathway to its extension to real-valued problems remains elusive. This paper aims to unleash this pathway by proposing a novel end-to-end Nature-Powered Graph Learning (NP-GL) framework. Specifically, through a three-dimensional co-design, NP-GL can leverage the nature's power of entropy increase to efficiently solve real-valued graph learning problems. Experimental results across 4 real-world applications with 6 datasets demonstrate that NP-GL delivers, on average, 6970X speedup and 10^5x energy consumption reduction with comparable or even higher accuracy than Graph Neural Networks (GNNs).

artificial intelligence↗
DOE OSTI2010

Bayesian Inference for Time Trends in Parameter Values using Weighted Evidence Sets

There is a nearly ubiquitous assumption in PSA that parameter values are at least piecewise-constant in time. As a result, Bayesian inference tends to incorporate many years of plant operation, over which there have been significant changes in plant operational and maintenance practices, plant management, etc. These changes can cause significant changes in parameter values over time; however, failure to perform Bayesian inference in the proper time-dependent framework can mask these changes. Failure to question the assumption of constant parameter values, and failure to perform Bayesian inference in the proper time-dependent framework were noted as important issues in NUREG/CR-6813, performed for the U. S. Nuclear Regulatory Commission’s Advisory Committee on Reactor Safeguards in 2003. That report noted that “in-dustry lacks tools to perform time-trend analysis with Bayesian updating.” This paper describes an applica-tion of time-dependent Bayesian inference methods developed for the European Commission Ageing PSA Network. These methods utilize open-source software, implementing Markov chain Monte Carlo sampling. The paper also illustrates an approach to incorporating multiple sources of data via applicability weighting factors that address differences in key influences, such as vendor, component boundaries, conditions of the operating environment, etc.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Purdue University: Results of B-Value Tests for Rock Saturation

This dataset includes an Excel file with the results of B-value tests to determine the magnitude of the back pressure required for full saturation of Sierra White granite samples. Test were conducted on cylindrical rock specimens of Sierra White granite in a custom water pressurized chamber. Dimensions and properties of Sierra White granite specimens are summarized in the PDF file. The PDF file also includes a description of the B-value tests, experimental procedure, and results.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Fundamental Solutions for Two-Point Boundary Value Problems in Orbital Mechanics

We consider a two-point boundary value problem (TPBVP) in orbital mechanics involving a small body (e.g., a spacecraft or asteroid) and N larger bodies. The least action principle TPBVP formulation is converted into an initial value problem via the addition of an appropriate terminal cost to the action functional. The latter formulation is used to obtain a fundamental solution, which may be used to solve the TPBVP for a variety of boundary conditions within a certain class. In particular, the method of convex duality allows one to interpret the least action principle as a differential game, where an opposing player maximizes over an indexed set of quadratics to yield the gravitational potential. In the case where the time duration is less than a specific bound, there exists a unique critical point for the resulting differential game, which yields the fundamental solution given in terms of the solutions of associated Riccati equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Investigating added value of regional climate modeling in North American winter storm track simulations

Extratropical Cyclone (EC) characteristics depend on a combination of large-scale factors and regional processes. However, the latter are considered to be poorly represented in global climate models (GCMs), partly because their resolution is too coarse. This work describes a framework using possibilities given by regional climate models (RCMs) to gain insight into storm activity during winter over North America (NA). Recent past climate period (1981–2005) is considered to assess EC activity over NA using the NCEP regional reanalysis (NARR) as a reference, along with the European reanalysis ERA-Interim (ERAI) and two CMIP5 GCMs used to drive the Canadian Regional Climate Model—version 5 (CRCM5) and the corresponding regional-scale simulations. While ERAI and GCM simulations show basic agreement with NARR in terms of climatological storm track patterns, detailed bias analyses show that, on the one hand, ERAI presents statistically significant positive biases in terms of EC genesis and therefore occurrence while capturing their intensity fairly well. On the other hand, GCMs present large negative intensity biases in the overall NA domain and particularly over NA eastern coast. In addition, storm occurrence over the northwestern topographic regions is highly overestimated. When the CRCM5 is driven by ERAI, no significant skill deterioration arises and, more importantly, all storm characteristics near areas with marked relief and over regions with large water masses are significantly improved with respect to ERAI. Conversely, in GCMdriven simulations, the added value contributed by CRCM5 is less prominent and systematic, except over western NA areas with high topography and over the Western Atlantic coastlines where the most frequent and intense ECs are located. Despite this significant added-value on seasonalmean characteristics, a caveat is raised on the RCM ability to handle storm temporal ‘seriality’, as a measure of their temporal variability at a given location. In fact, the driving models induce some significant footprints on the RCM skill to reproduce the intra-seasonal pattern of storm activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Long-term missing value imputation for time series data using deep neural networks

We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron, for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to three years) in three environmental datasets with different time series characteristics, namely daily groundwater levels, daily soil moisture, and hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled values obtained with our approach to the widely used R-based time series gap filling methods ImputeTS and mtsdi. The results indicate that using an MLP for filling a large gap leads to better results, especially when the data behave nonlinearly. Thus, our approach enables the use of datasets that have a large gap in one variable, which is common in many long-term environmental monitoring observations.

97 MATHEMATICS AND COMPUTING↗