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BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

The BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,52...

Tripp, C. et al National Renewable Energy Laboratory

Dec 30, 2022
9 Resources
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BUTTER Empirical Deep Learning Dataset

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four le...

Tripp, C. et al National Renewable Energy Laboratory

May 20, 2022
4 Resources
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulat...

Beckers, K. et al National Renewable Energy Laboratory

Feb 18, 2021
1 Resources
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Description of the NSW Australia Electricity Network

Information about the New South Wales (NSW) electricity network is available. The information was taken from a line diagram of the NSW (Australia) Electricity network circa 2002 containing electrical bus and transmission line properties for use in load flow analysis. The results o...

Neale, D. and Management, M. National Renewable Energy Laboratory

Jan 01, 2002
1 Resources
0 Stars
In curation

NSW Australia Electricity Network Bus and Transmission Line Data: Snapshot from Mar 2011

[Moxy Knowledge Management](http://www.moxy.com.au/ "Moxy website") provides a general instance of the New South Wales (NSW), Australia, electricity network and an assessment of the robustness of the NSW electricity grid (e.g.,the results of several different outage scenarios). De...

Hallett, K. and Management, M. National Renewable Energy Laboratory

Dec 31, 2001
2 Resources
0 Stars
In curation

Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets

This dataset contains turbine and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in ...

Ramos, D. et al National Renewable Energy Laboratory

Feb 12, 2021
5 Resources
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REopt Lite Geothermal Heat Pump Design Requirements

This document describes the design requirements for the geothermal heat pump (GHP) module being added to the existing REopt Lite web tool. This document describes the purpose, users, and functional requirements to which the modified web tool shall conform. This document will be re...

Olis, D. National Renewable Energy Laboratory

Mar 08, 2021
6 Resources
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Locations of water supplies serving Nairobi, Kenya.

This dataset portrays locations of water supplies serving Nairobi, Kenya. Locations of dam were approximately located by WRI based on SoK 1971, MoWD and JICA 1992a, 1992b, 1992c, Nyaoro 1999, and Wambua 2003. This data was used in Map 3.9 in Nature's Benefits in Kenya: An Atlas of...

S, L. National Renewable Energy Laboratory

Dec 26, 2018
1 Resources
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In curation

DEEPEN Leapfrog Geodata Model Cleaned and Reformatted Exploration Datasets from Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific e...

Pauling, H. et al National Renewable Energy Laboratory

Jun 30, 2023
22 Resources
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Global innovation contest for future solar energy solutions

Stockholm Royal Seaport is one of Europe’s largest urban development projects and is a leader when it comes to next generation sustainable neighborhoods. As part of Stockholm Royal Seaport’s sustainability focus the competition is organized to find long-term solar energy solut...

Competitions, S. National Renewable Energy Laboratory

Dec 21, 2014
1 Resources
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells increasing or decreasing the fluid flow rates across the wells and drilling new wells at appropriate locations. Th...