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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗
NASA NTRS2024

Ensuring Safe Decision-Making on the Moon and Mars: Cognitive Performance Assessment for Exploration Class Mission EVA

Extravehicular activity (EVA) is one of the most dangerous and cognitively demanding actions that astronauts can execute, and the cognitive demands associated with future partial gravity EVA on the Moon and Mars are expected to be higher compared to microgravity EVA currently conducted from the International Space Station (ISS). Decrements in cognitive performance present an important risk to crew safety during exploration mission class EVA. Yet there is currently insufficient data to characterize cognitive performance prior to, during, and following EVA. Furthermore, it is still unclear which cognitive domains are most important for conducting mission critical decisions with crew safety implications. To address this gap, we conducted a cognitive task analysis (CTA) of EVA to characterize the procedures, the cognitive demands required, and the critical safety decisions associated with decrements in cognitive performance. We used the applied cognitive task analysis method as outlined by Militello and Hutton (1998) and conducted interviews with subject matter experts (SMEs) in EVA at NASA Johnson Space Center in summer 2024. Interviews focused on exploration EVA and elicited feedback from SMEs on the cognitive skills required for specific EVA tasks including cognitive strategies, critical cues, and decision-making strategies. A cognitive demands table was assembled to consolidate and synthesize the information from all interviews. The information from this cognitive task analysis will inform ongoing work to identify the key cognitive domains for safe decision-making during exploration mission class EVA on the Moon and Mars. This work will importantly provide a link between cognitive domains identified in the cognitive task analysis and operationally-relevant performance metrics collected during spaceflight and simulation EVA that can be tracked and assessed by NASA mission operations. Ultimately, this work will aid in the development and application of exploration class cognitive performance monitoring capabilities to improve safe decision-making and inform EVA planning on future spaceflight missions to the Moon and Mars.

Steven R Anderson↗
NASA NTRSDate not supplied

Real-Time Science Decisioning During High Tempo-High Intensity Mission Operations and the Role of Analogs

Introduction: NASA’s VIPER mission presents a unique operational paradigm within the history of robotic spaceflight. The proximity of the Moon to the Earth and the terrain elements (surface characteristics, light/shadow dynamics, communication links) of the lunar South Polar landing site create unprecedented operational conditions between these two planetary bodies. Apollo era lunar science and exploration included humans in situ to operate instruments and assimilate observational inputs in real-time. Previous lunar orbital missions have worked to operational timescales, e.g., decisional timelines and communication exchanges, that were weeks in length. Mars rover missions have worked to operational timescales, e.g., decisional timelines and communication exchanges between Mars and Earth, that were hours, days, and weeks in length. In the case of the VIPER mission, our operational decisioning for rover driving and instrument commanding will be compressed to minute-scale timeframes. These operational conditions directly impact the manner and speed with which the VIPER Science Team (VST) is required to synthesize and analyze data and produce timely science-driven decisions throughout surface mission operations. The VST shall provide mission enhancing scientific input to guide rover traverse planning and drill site confirmation and selection throughout surface operations. Further, the VST input will be of vital importance to the mission’s ability to maximize science return and to meet broader NASA objectives for future lunar in-situ resource utilization (ISRU)and exploration activities. The VST co-located in the Mission Science Center (MSC) will be responsive to the tactical operational cadence of the Mission Operations Center (MOC) and will provide further strategic and Long-Term Planning (LTP) guidance to the mission. The VIPER Science Operations & Integration(SO&I)team has developed an architecture that is focused on the infusion of science-decisioning into the operational framework and execution cadence of VIPER. NASA analog research has played a significant role in the construction of the VIPER science operations systems. As an example, the SO&I team has led analog missions that have focused on bringing together expertise in the sciences (natural, applied and social) and in operations in service of learning how to build and hold together interdisciplinary work environments and what tools are needed to support high tempo, high intensity integrated decisioning. These experiences have provided an essential foundation of knowledge to the VIPER team. Those analogs that specifically influenced the VIPER science operations construct were identified through a process of comparative analysis to prioritize those that offered relevance in whole or in part, and those that did not. The analog research output that provided extensibility to the VIPER science operations architecture included remote teams of humans and robots in cooperation (synchronous and asynchronous) with simulated earthbound systems, engineering and science teams, and the integrated assembly of tools that supported scientific analysis and data synthesis and provided infrastructure for the remote testing framework. Analogs which included real-time data monitoring, synthesis, visualization and access in a democratized and operationalized manner were of particular interest to the development of the VIPER MSC toolset both in terms of the technology and the processes used to develop the supporting infrastructure. We anticipate that each subsequent mission to the lunar south pole, whether with robots or humans, will be able to optimize science and exploration return by evolving strategies to infuse real-time collaborative science-decisioning. Furthermore, these efforts will result in a foundation for science operations development in support of human-robotic exploration of deep space and Mars. NASA analogs can continue to provide the opportunity to prepare, test and iterate on the operational concepts and tools that will support these ever-expanding space exploration efforts. Our presentation will include an overview of the VIPER Science Operations & Integration development process and specifics on what aspects of analog research have had a significant impact on our work systems.

D S S Lim↗
DOE OSTI2015

Model-assisted decision analyses related to a chromium plume at Los Alamos national Laboratory - 15449

?he chromium plume at Los Alamos National Laboratory is a complex contaminant-remediation site with spatial extent of several square kilometers. It is located in a regional aquifer near water-supply wells and other points of compliance. The plume originated on the ground surface and the contaminant migrated along complex pathways through a thick vadose zone (∼300 m) that includes intermediate lateral zones of saturation and vertical preferential flow paths. Here we discuss the development of a modeling computational framework for the site. The framework work incorporates data assimilation and model-results analysis tools including decision support techniques. The decision tools are demonstrated using synthetic problems consistent with the interpretation of existing site data and site modeling results. The analyses aim at robust and efficient environmental management of the plume and explore a series of alternative remedial options. The remedial options include natural attenuation (NA), enhanced attenuation (EA), contaminant source removal (in the vadose zone), contaminant extraction (in the regional aquifer at the plume centroid and peripheries), biogeochemical remediation (injection of fluids stimulating growth of organisms in the aquifer impacting chromium concentrations), as wells as hydraulic controls on the groundwater flow and transport in the vadose zone and the regional aquifer. The applied model analyses and decision-support algorithms are implemented in code MADS (Model Analyses for Decision Support; http://mads.lanl.gov). MADS is an open-source framework for model-based decision support employing system and physics simulation models. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

A comprehensive academic and industrial survey of blockchain technology for the energy sector using fuzzy Einstein decision-making

The global energy sector is undergoing a significant transformation driven by decarbonization and digitalization, leading to the emergence of Distributed Ledger Technology (DLT) — particularly blockchain — as a promising tool for enhancing transparency, security, and efficiency in modern power systems. This study aims to provide a comprehensive academic and industrial survey of blockchain applications in the energy sector and develop a robust decision-making framework to identify and prioritize the most promising real-world use cases based on multidisciplinary criteria. A three-stage methodology was adopted: (i) a literature and market review encompassing over 300 academic publications and commercial blockchain initiatives in energy, (ii) an in-depth evaluation of the evolution and viability of blockchain initiatives in energy with the help of expert surveys, and (iii) a novel decision-making model using a q-rung orthopair fuzzy Multi-Attributive Border Approximation (q-ROF-MABAC) method under the Einstein operator. The results were compared with existing decision models to validate consistency and robustness. Nine key blockchain use case categories were identified and ranked based on technical, economic, and governance dimensions. The results demonstrated that integrating expert insights into a fuzzy logic framework helps filter out overhyped claims in the literature and prioritize realistic and high-impact applications such as green certificates, grid services, and peer-to-peer energy trading. The model’s rankings remained stable across varying weight configurations, confirming the robustness of the methodology. This study provides an evidence-based decision-support tool for researchers, industry stakeholders, and policymakers to better understand, evaluate, and adopt blockchain technologies in the energy sector.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2005

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

AVHRR (ADVANCED VERY HIGH RESOLUTION RADIOMETER)↗
DOE OSTI2018

Harnessing expert knowledge: Defining a Bayesian network decision model with limited data-Model structure for the vibration qualification problem

As systems become more complex, systems engineers rely on experts to inform decisions. There are few experts and limited data in many complex new technologies. This challenges systems engineers as they strive to plan activities such as qualification in an environment where technical constraints are coupled with the traditional cost, risk, and schedule constraints. Bayesian network (BN) models provide a framework to aid systems engineers in planning qualification efforts with complex constraints by harnessing expert knowledge and incorporating technical factors. By quantifying causal factors, a BN model can provide data about the risk of implementing a decision supplemented with information on driving factors. This allows a systems engineer to make informed decisions and examine “what-if” scenarios. This paper discusses a novel process developed to define a BN model structure based primarily on expert knowledge supplemented with extremely limited data (25 data sets or less). The model was developed to aid qualification decisions—specifically to predict the suitability of six degrees of freedom (6DOF) vibration testing for qualification. The process defined the model structure with expert knowledge in an unbiased manner. Finally, validation during the process execution and of the model provided evidence the process may be an effective tool in harnessing expert knowledge for a BN model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Modular evaluation method for subsurface activities (MEMSA). A novel approach for integrating social acceptance in a permit decision-making process for subsurface activities

We investigate how the decision support system ‘Modular Evaluation Method Subsurface Activities’ (MEMSA) can help facilitate an informed decision-making process for permit applications of subsurface activities. To this end, we analyze the extent the MEMSA approach allows for a dialogue between stakeholders in a transparent manner. We use the exploration permit for the underground gas storage facility at the Pieterburen salt dome (Netherlands) as a case study. The results suggest that the MEMSA approach is flexible enough to adjust to changing conditions. Furthermore, MEMSA provides a novel way for identifying structural problems and possible solutions in permit decision-making processes for subsurface activities, on the basis of the sensitivity analysis of intermediate rankings. We suggest that the planned size of an activity should already be specified in the exploration phase, because this would allow for a more efficient use of the subsurface as a whole. We conclude that the host community should be involved to a greater extent and in an early phase of the permit decision-making process, for example, already during the initial analysis of the project area of a subsurface activity. We suggest that strategic national policy goals are to be re-evaluated on a regular basis, in the form of a strategic vision for the subsurface, to account for timing discrepancies between the realization of activities and policy deadlines, because this discrepancy can have a large impact on the necessity and therefore acceptance of a subsurface activity.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Interactive Decision-Support Tool for Risk-Based Radiation Therapy Plan Comparison for Hodgkin Lymphoma

Purpose: To present a novel tool that allows quantitative estimation and visualization of the risk of various relevant normal tissue endpoints to aid in treatment plan comparison and clinical decision making in radiation therapy (RT) planning for Hodgkin lymphoma (HL). Methods and Materials: A decision-support tool for risk-based, individualized treatment plan comparison is presented. The tool displays dose–response relationships, derived from published clinical data, for a number of relevant side effects and thereby provides direct visualization of the trade-off between these endpoints. The Quantitative Analyses of Normal Tissue Effects in the Clinic reports were applied, complemented with newer data where available. A “relevance score” was assigned to each data source, reflecting how relevant the input data are to current RT for HL. Results: The tool is applied to visualize the local steepness of dose–response curves to drive the reoptimization of a volumetric modulated arc therapy treatment plan for an HL patient with head-and-neck involvement. We also use this decision-support tool to visualize and quantitatively evaluate the trade-off between a 3-dimensional conformal RT plan and a volumetric modulated arc therapy plan for a patient with mediastinal HL. Conclusion: This multiple-endpoint decision-support tool provides quantitative risk estimates to supplement the clinical judgment of the radiation oncologist when comparing different RT options.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Evacuation decision behavior for no-notice emergency events

No-notice emergency events refer to unpredictable disasters such as earthquakes, chemical spills, or terrorist attacks, where it is impracticable to forewarn the public about their occurrence and design evacuation plans for them. This calls for an in-depth investigation of people’s evacuation behavior and identifying the most influential factors in their evacuation planning process to develop policy-sensitive pre-disaster plans for such events. As a response to this need, this study investigates individuals’ evacuation decision behavior in the context of no-notice emergency events. Since it is highly expected that people will have heterogenous decision behavior in such situations, we first apply a clustering algorithm to classify them into three maximally homogeneous clusters. Then, cluster-specific multivariate ordered probit models are developed to estimate the likelihood of selecting any of the three options of ignoring the situation, seeking shelter at the place, and evacuating to a safe place. The model estimation results indicate that a wide spectrum of factors affect the evacuation decision including individuals’ socio-economic attributes, disaster characteristics, built-environment factors, and government evacuation order. Further, variations of estimated coefficients across the population clusters highlight the significant dissimilarities in decision behavior of their associated members.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

A dataset for understanding self-reported patterns influencing residential energy decisions

Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household energy decision-making patterns are available. The dataset (UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency) presents 9,919 responses from U.S. residents of single-family and small multifamily homes. Derived from a national-scale internet survey, the dataset contains 391 variables: demographics, building characteristics, home modifications, willingness to adopt new technologies, motivations for making changes, barriers, program participation, trusted information sources, and energy scenarios. Responses were validated via internal consistency checks and comparison with other U.S. national scale datasets. UPGRADE-E advances knowledge of household energy related decision-making, tying demographics, home modifications, and self-reported cognitive drivers together at a scale and breadth that has not been previously achieved. Policymakers and researchers at local, regional, and national levels may leverage this dataset to understand drivers influencing the adoption of key technologies in U.S. homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Decision making based on optical excitation transfer via near-field interactions between quantum dots

Optical near-field interactions between nanostructured matters, such as quantum dots, result in unidirectional optical excitation transfer when energy dissipation is induced. This results in versatile spatiotemporal dynamics of the optical excitation, which can be controlled by engineering the dissipation processes and exploited to realize intelligent capabilities such as solution searching and decision making. Here, we experimentally demonstrate the ability to solve a decision making problem on the basis of optical excitation transfer via near-field interactions by using colloidal quantum dots of different sizes, formed on a geometry-controlled substrate. We characterize the energy transfer behavior due to multiple control light patterns and experimentally demonstrate the ability to solve the multi-armed bandit problem. Our work makes a decisive step towards the practical design of nanophotonic systems capable of efficient decision making, one of the most important intellectual attributes of the human brain.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

A Distributionally Robust Resilience Enhancement Strategy for Distribution Networks Considering Decision-Dependent Contingencies

When performing the resilience enhancement for distribution networks, there are two obstacles to reliably model the uncertain contingencies: 1) decision-dependent uncertainty (DDU) due to various line hardening decisions, and 2) distributional ambiguity due to limited outage information during extreme weather events (EWEs). Here, to address these two challenges, this paper develops scenario-wise decision-dependent ambiguity sets (SWDD-ASs), where the DDU and distributional ambiguity inherent in EWE-induced contingencies are simultaneously captured for each possible EWE scenario. Then, a two-stage tri-level decision-dependent distributionally robust resilient enhancement (DD-DRRE) model is formulated, whose outputs include the optimal line hardening, distributed generation (DG) allocation, and proactive network reconfiguration strategy under the worst-case distributions in SWDD-ASs. Subsequently, the DD-DRRE model is equivalently recast to a mixed-integer linear programming (MILP)-based master problem and multiple scenario-wise subproblems, facilitating the adoption of a customized column-and-constraint generation (C&CG) algorithm. Finally, case studies demonstrate a remarkable improvement in the out-of-sample performance of our model, compared to its prevailing stochastic and robust counterparts. Moreover, the potential values of incorporating the ambiguity and distributional information are quantitatively estimated, providing a useful reference for planners with different budgets and risk-aversion levels.

decision-dependent uncertainty↗
DOE OSTI2020

A Decision Support Tool for Distributed Solar and Storage Investments: A Case Study in Austin, TX

This study builds a decision support tool to evaluate when it is a good economic decision (least cost with minimum discomfort) for the residential customer to invest in distributed energy resources (DERs) based on different electricity rate structures, DER ownership frameworks, and DER rebates offered by electric utilities. Here, the tool is demonstrated using empirical electricity consumption data from Pecan Street Inc. (a non-profit entity based on Austin, Texas), residential rates from Austin Energy (the municipal electric utility in Austin, Texas), DER ownership costs from various nationwide pilot programs, and incentives offered by electric utilities in the United States. Results show that for constant electricity rates, the overall expenditure is least when the customer owns solar panels without storage, while for time-varying pricing structures, the least expensive scenario is one where the customer does not own any DERs. As the capital costs for DERs decline, utilities incentivize customer ownership of DERs, and more residential customers face the decision of whether to invest in DERs, this study aims to be a key tool in aiding that decision-making process.

14 SOLAR ENERGY↗
DOE OSTI2014

SU-D-BRD-05: Decision Opportunities in Radiation Therapy Treatments

Purpose: A method to reveal tradeoffs in radiation therapy treatments is introduced in order to aid in clinical, patient-specific decision making. Methods: A clinically acceptable treatment plan was varied for two patients, a stereotactic body radiation therapy (SBRT) lung cancer case and a pituitary case, in order to reveal decision opportunities. Plans were optimized such that non-zero dose-volume objectives were defined for all organs at risk (OARS). At fixed planning target volume (PTV) dose, a single OAR is sacrificed, i.e. the weight of the dose volume objective is deceased, and potential dosimetric benefits in other regions of interest are identified. If tradeoffs are identified, plans are stored and presented as decision opportunities. Results: Clinically relevant tradeoffs were revealed by sacrificing individual OARs. The SBRT lung case was planned according to the Radiotherapy-Oncology Group (RTOG) 0813 protocol, but by violating the high-dose protocol objective (>2 cm from the PTV) in the patient's lung, mean heart dose was reduced by 1.7 Gy and the great vessel V20 was reduced from 42% to 2%. Tradeoffs in dose to the chestwall and heart were also revealed, an increase of 6 Gy in chestwall-Dmax reduces heart mean dose by 0.9 Gy and mean dose to the great vessels by 2.6 Gy. For the pituitary tumor, sacrificing the right parotid gland (increasing mean dose from 7.8 Gy to 14.1 Gy) spares the temporal lobes bilaterally (V20 is reduced by 4%) and left parotid mean dose is reduced from 6.4 Gy to 5.2 Gy. Conclusion: Clinical tradeoffs in radiation therapy treatment planning are revealed by sacrificing individual OARS. By revealing these tradeoffs, decision making in plan selection is simplified and can be considered in the context of patient-specific quality of life.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-155: Automatic Quantitative Decision Making Metric for 4DCT Image Quality

Purpose: To develop a quantitative decision making metric for automatically detecting irregular breathing using a large patient population that received phase-sorted 4DCT. Methods: This study employed two patient cohorts. Cohort#1 contained 256 patients who received a phasesorted 4DCT. Cohort#2 contained 86 patients who received three weekly phase-sorted 4DCT scans. A previously published technique used a single abdominal surrogate to calculate the ratio of extreme inhalation tidal volume to normal inhalation tidal volume, referred to as the κ metric. Since a single surrogate is standard for phase-sorted 4DCT in radiation oncology clinical practice, tidal volume was not quantified. Without tidal volume, the absolute κ metric could not be determined, so a relative κ (κrel) metric was defined based on the measured surrogate amplitude instead of tidal volume. Receiver operator characteristic (ROC) curves were used to quantitatively determine the optimal cutoff value (jk) and efficiency cutoff value (τk) of κrel to automatically identify irregular breathing that would reduce the image quality of phase-sorted 4DCT. Discriminatory accuracy (area under the ROC curve) of κrel was calculated by a trapezoidal numeric integration technique. Results: The discriminatory accuracy of ?rel was found to be 0.746. The key values of jk and tk were calculated to be 1.45 and 1.72 respectively. For values of ?rel such that jk≤κrel≤τk, the decision to reacquire the 4DCT would be at the discretion of the physician. This accounted for only 11.9% of the patients in this study. The magnitude of κrel held consistent over 3 weeks for 73% of the patients in cohort#3. Conclusion: The decision making metric, ?rel, was shown to be an accurate classifier of irregular breathing patients in a large patient population. This work provided an automatic quantitative decision making metric to quickly and accurately assess the extent to which irregular breathing is occurring during phase-sorted 4DCT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Minimizing Uncertainties Impact in Decision Making with an Applicability Study for Economic Power Dispatch

This report summaries the work performed under the LDRD project on the preliminary study on knowledge automation, where specific focus has been made on the investigation of the impact of uncertainties of human decision making onto the optimization of the process operation. At first the statistics on signals from the Brain-Computing Interface (BCI) is analyzed so as to obtain the uncertainties characterization of human operators during the decision making phase using the electroencephalogram (EEG) signals. This is then followed by the discussions of an architecture that reveals the equivalence between optimization and closed loop feedback control design, where it has been shown that all the optimization problems can be transferred into the control design problem for closed loop systems. This has led to a “closed loop” framework, where the structure of the decision making is shown to be subjected to both process disturbances and controller’s uncertainties. The latter can well represent the uncertainties or randomness occurred during human decision making phase. As a result, a stochastic optimization problem has been formulated and a novel solution has been proposed using probability density function (PDF) shaping for both the cost function and the constraints using stochastic distribution control concept. A sufficient condition has been derived that guarantees the convergence of the optimal solution and discussions have been made for both the total probabilistic solution and chanced constrained optimization which have been well-studied in optimal power flows (OPF) area. A simple case study has been carried out for the economic dispatch of powers for a grid system when there are distributed energy resources (DERs) in the system, and encouraging results have been obtained showing that a significant savings on the generation cost can be expected.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Renewable Energy Data, Analysis, and Decisions: A Guide for Practitioners

High-quality renewable energy resource data and other geographic information system (GIS) data are essential for the transition to a clean energy economy that prioritizes local resources, improves resiliency, creates jobs, and promotes energy independence. These data are crucial for making informed decisions—ranging from policy and investment decisions to reliable power sector planning. Decisions that are data-driven reflect appropriate ambition, maximize cost-effectiveness, and enable successful implementation of renewable energy investments. This guide is intended to support policymakers and planners, as well as technical experts, consultants, and academics in incorporating improved data and analysis into renewable energy decision-making.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗