Data released under the Department of Energy's (DOE) Open Energy Data Initiative (OEDI). The Open Energy Data Initiative aims to improve and automate access of high-value energy data sets across the U.S. Department of Energy’s programs, offices, and national laboratories. OEDI aims to make data actionable and discoverable by researchers and industry to accelerate analysis and advance innovation.

Energy Data Lake Cloud Platform Market Outlook

According to our latest research, the global energy data lake cloud platform market size reached USD 2.9 billion in 2024, demonstrating a robust expansion driven by the growing digitization of the energy sector and the surging need for advanced data analytics. The market is anticipated to grow at a remarkable CAGR of 21.4% from 2025 to 2033, propelling the market to a forecasted value of USD 20.6 billion by 2033. This rapid growth is primarily fueled by the increasing adoption of cloud-based data management solutions by energy companies aiming to optimize operations, enhance grid reliability, and support the integration of renewable energy sources.

One of the primary growth factors for the energy data lake cloud platform market is the exponential rise in data generated across the energy value chain. With the proliferation of IoT sensors, smart meters, and grid automation technologies, energy companies are now inundated with vast volumes of structured and unstructured data. Traditional data management systems are often inadequate for handling such scale and complexity, driving the shift towards cloud-based data lake platforms. These platforms offer scalable storage and advanced analytics capabilities, enabling organizations to extract actionable insights, improve asset performance, and minimize operational risks. Furthermore, the evolution of artificial intelligence and machine learning tools integrated with cloud data lakes empowers energy firms to predict equipment failures, optimize maintenance schedules, and enhance overall operational efficiency.

Another significant driver is the growing emphasis on regulatory compliance and risk management within the energy industry. With stringent regulations regarding emissions, safety, and data privacy, energy companies are compelled to adopt robust data management frameworks. Energy data lake cloud platforms facilitate seamless data integration, traceability, and real-time reporting, ensuring adherence to regulatory standards while minimizing compliance costs. These platforms also support advanced risk analytics, enabling organizations to proactively identify potential threats and mitigate them effectively. The ability to consolidate disparate data sources into a unified, secure cloud environment further enhances transparency and supports informed decision-making at every level of the organization.

The market’s growth is also being propelled by the accelerating transition towards renewable energy and decentralized energy systems. As utilities and independent power producers integrate more distributed energy resources (DERs) such as solar, wind, and battery storage, the complexity of grid management increases substantially. Energy data lake cloud platforms provide the necessary infrastructure to aggregate, process, and analyze data from diverse sources in real-time, facilitating efficient grid balancing, demand response, and predictive maintenance. This capability is crucial for ensuring grid stability and reliability in an era of fluctuating renewable energy supply. Additionally, the global push towards sustainability and carbon neutrality is compelling energy companies to embrace digital transformation initiatives, further amplifying the demand for advanced cloud-based data solutions.

From a regional perspective, North America currently leads the energy data lake cloud platform market, accounting for a substantial share in 2024. The region’s dominance is attributed to early adoption of advanced digital technologies, robust cloud infrastructure, and significant investments in smart grid modernization. Europe follows closely, driven by stringent regulatory frameworks and ambitious renewable energy targets. The Asia Pacific region is expected to witness the fastest growth over the forecast period, fueled by rapid urbanization, expanding energy demand, and increasing investments in digital infrastructure. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, supported by ongoing energy sector reforms and the adoption of innovative data management solutions.

Data Lakes Market size was valued at USD 17.21 Billion in 2024 and is projected to reach USD 79.09 Billion by 2031, growing at a CAGR of 21.00% during the forecasted period 2024 to 2031.

The data lakes market is driven by the growing need for organizations to manage and analyze vast amounts of unstructured and structured data for better decision-making and insights. As businesses increasingly rely on big data analytics, machine learning, and artificial intelligence to gain competitive advantages, data lakes provide a scalable and cost-effective solution to store raw data from diverse sources. The rising adoption of cloud-based solutions further fuels the market, as cloud data lakes offer flexibility, agility, and seamless integration with analytics tools. Additionally, the growing emphasis on digital transformation, real-time data processing, and enhanced data governance are key factors pushing the demand for data lakes across industries such as finance, healthcare, retail, and manufacturing.

The United States is embarking on an ambitious transition to a 100% clean energy economy by 2050, which will require improving the flexibility of electric grids. One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, it is critical to understand how and when energy is used. High quality end-use load profiles (EULPs) provide this information, and can help cities, states, and utilities understand the time-sensitive value of energy efficiency, demand response, and distributed energy resources. Publicly available EULPs have traditionally had limited application because of age and incomplete geographic representation. To help fill this gap, the U.S. Department of Energy (DOE) funded a three-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly available dataset of calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses, across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets, as described in the "Technical Report Documenting Methodology" linked in the submission.

The BuildingsBench datasets consist of: Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock. 7 real residential and commercial building datasets for benchmarking two downstream tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. Buildings-900K can be used for pretraining models on day-ahead STLF for residential and commercial buildings. The specific gap it fills is the lack of large-scale and diverse time series datasets of sufficient size for studying pretraining and finetuning with scalable machine learning models. Buildings-900K consists of synthetically generated energy consumption time series. It is derived from the NREL End-Use Load Profiles (EULP) dataset (see link to this database in the links further below). However, the EULP was not originally developed for the purpose of STLF. Rather, it was developed to "...help electric utilities, grid operators, manufacturers, government entities, and research organizations make critical decisions about prioritizing research and development, utility resource and distribution system planning, and state and local energy planning and regulation." Similar to the EULP, Buildings-900K is a collection of Parquet files and it follows nearly the same Parquet dataset organization as the EULP. As it only contains a single energy consumption time series per building, it is much smaller (~110 GB). BuildingsBench also provides an evaluation benchmark that is a collection of various open source residential and commercial real building energy consumption datasets. The evaluation datasets, which are provided alongside Buildings-900K below, are collections of CSV files which contain annual energy consumption. The size of the evaluation datasets altogether is less than 1GB, and they are listed out below: ElectricityLoadDiagrams20112014 Building Data Genome Project-2 Individual household electric power consumption (Sceaux) Borealis SMART IDEAL Low Carbon London A README file providing details about how the data is stored and describing the organization of the datasets can be found within each data lake version under BuildingsBench.

These data provide the 2024 update of the Electricity Annual Technology Baseline (ATB). Starting in 2015 NREL has presented the ATB, consisting of detailed cost and performance data, both current and projected, for electricity generation and storage technologies. The ATB products now include data (Excel workbook, Tableau workbooks, and structured summary csv files), as well as documentation and user engagement via a website, presentation, and webinar. Starting in 2021, the data are cloud optimized and provided in the OEDI data lake. The data for 2015 - 2020 are can be found on the NREL Data Search Page. The website documentation can be found on the ATB Website.

This dataset contains seismic-reflection records created in 2010 around the Soda Lake geothermal field near Fallon, Nevada. The data was collected by the power plant operator at the time, Magma Energy (CYRQ Energy in 2024). This was a petroleum-industry-quality three-dimensional (3D) and three-component (3C) seismic reflection survey covering about 36 square miles. Most of the volume of this raw data set consists of 3D seismic records saved as hundreds of SEG-Y files, with one 3D seismic record file per vibrator source location, called "shot records". The data is in SEG-Y format, with each shot record containing three geophone components for all the geophone sensors active for that shot. In addition to the raw data, provided below are folders containing all of the field logs, metadata, and survey reports produced during the project.

Starting in 2015 NREL has presented the Annual Technology Baseline (ATB) in an Excel workbook that contains detailed cost and performance data, both current and projected, for renewable and conventional technologies. The workbook includes a spreadsheet for each technology. This version of the workbook provides the final updates to data for the 2021 ATB. In 2019 and 2020, NREL has also provided selected data in Tableau workbooks and structured summary csv files. The data for 2015 - 2020 is located on https://data.nrel.gov. In 2021 and going forward, the data is cloud optimized and provided in the OEDI data lake. A website documents this and future data at https://atb.nrel.gov.

These data provide the 2023 update of the Electricity Annual Technology Baseline (ATB). Starting in 2015 NREL has presented the ATB, consisting of detailed cost and performance data, both current and projected, for electricity generation and storage technologies. The ATB products now include data (Excel workbook, Tableau workbooks, and structured summary csv files), as well as documentation and user engagement via a website, presentation, and webinar. Starting in 2021, the data are cloud optimized and provided in the OEDI data lake. The data for 2015 - 2020 are can be found on the NREL Data Search Page. The website documentation can be found on the ATB Website.

The datasets - Uganda Lakes, are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders. Link: http://data-energy-gis.opendata.arcgis.com/ The dataset is published on October 23, 2014

HILARRI is a database of links between major datasets of operational hydropower dams and powerplants, and inland water bodies. These connections are critical for conducting large-scale analysis of hydropower infrastructure and their associated natural and engineered water systems. Features include: – Dams from the National Inventory of Dams (2024) and the Global Reservoir and Dam Database (GRanD v1.3) – Hydropower plants from the Existing Hydropower Assets dataset (EHA 2024) These hydropower infrastructure features are linked to several major datasets that provide hydrologic and hydraulic information relevant for analysis of hydropower systems that includes the integral water resources. That information comes from: – Products from the National Hydrography Dataset (NHD) – NHDPlusV2 Medium Resolution river network flowlines, – NHD waterbodies (limited to lakes and reservoirs), – NHD Watershed Boundary Dataset (HUC12-level for the Conterminous United States (CONUS)) – NHD High Resolution waterbodies – HydroLAKES water bodies (lakes and reservoirs) – LAGOS-US lakes and reservoirs – EPA National Lakes Assessment (2007, 2012, 2017, and 2022) – The Reservoir Sedimentation Database (RESSED) Unique identifiers are used to facilitate joining to the original full datasets. For example, characteristics of NHD flowlines such as estimated average flow rate can be joined from the NHDPlusV2 dataset to a dam or power plant listed in HILARRI based on the ID field, “COMID”, that is common to both datasets. HILARRI only includes basic information about identifiers, location, and data quality or usage notes. It does not contain the attributes or time series data associated with these sites. The HILARRI dataset incorporates information from several datasets to facilitate more effective and accurate analysis of hydropower infrastructure and their associated waterbodies. For example, dams were checked against the most recent American Rivers Dam Removal Database to identify and flag facilities that may no longer exist. Additionally, dams that are listed multiple times in the NID are identified and flagged to avoid double-counting when analyzing and summarizing information. Other quality flags include certainty of operational hydropower (i.e., if one or more datasets indicates hydropower at a particular location), whether an associated water body is accurate or composed of multiple polygons, or whether there is a known issue with reported characteristics in one of the underlying datasets. These additional data flags are designed to increase confidence in data usage for individual to large-scale analyses.

This data set includes information on sampling locations, water chemistry and chlorophyll collected at 18 locations in the Great Lakes-St. Lawrence River and 4 locations in Lake Simcoe.

The dataset represents the lakes participating in the Citizen Statewide Lake Monitoring Assessment Program (CSLAP). CSLAP is a volunteer lake monitoring and education program that is managed by DEC and New York State Federation of Lake Associations (NYSFOLA). The data collected through the program is used to identify water quality issues, detect seasonal and long term patterns, and inform volunteers and lake residents about water quality conditions in their lake. The program has delivered high quality data to many DEC programs for over 25 years.The dataset catalogs CSLAP lake information; including: lake name, lake depth, public accessibility, trophic status, watershed area, elevation, lake area, water quality classification, county, town, CSLAP status, years sampled, and last year sampled.

This dataset contains meteorological data collected for three Arctic lakes and compiled to satisfy input requirements of the LAKE 2.0 model. The dataset was generated to act as a benchmarking dataset for future model-data inter-comparisons. The LAKE 2.0 model simulates temperatures within the water later and the sedimentary layer of a lake. The LAKE2.0. is an open-source code and available to download via this weblike http://tesla.parallel.ru/Viktor/LAKE/-/wikis/LAKE-model (last visit July 14, 2021). The meteorological data are required to simulate the surface energy balance at the surface of a lake. This dataset includes a compilation of the meteorological data pulled from multiple data streams, including National Oceanic and Atmospheric Administration (NOAA) climate data, Circumarctic Lakes Observation Network (CALON) data, and the United States Geological Survey (USGS) data. The data were compiled for three Arctic lakes: FoxDen (66.55877, -164.45670), Atqasuk (70.452497, -156.951984), and Toolik (68.63150, -149.60740). Each meteorological data is in comma-delimited format (file extension ‘.dat’) and includes eight columns: Temperature [K], Pressure [Pa], longwave downward radiation [W/m2], shortwave downward radiation [W/m2], “U” wind speed [m/s], ”V” wind speed [m/s], humidity [kg/kg], precipitation [m/s]. In addition to the meteorological data file, we included setup and driver files. The Toolik lake is the deepest out of three lakes and has inflowing and outflowing groundwater data. InflowOutflowREADME.txt has more information about inflow and outflow flies. The other two lakes are much shallower and modeled as a closed system (i.e. no water inflow or outflow).

Global transition towards renewable energy production has increased the demand for new and more flexible hydropower operations. Before management and stakeholders can make informed choices on potential mitigations, it is essential to understand how the hydropower reservoir ecosystems respond to water level regulation (WLR) impacts that are likely modified by the reservoirs' abiotic and biotic characteristics. Yet, most reservoir studies have been case-specific, which hampers large-scale planning, evaluation and mitigation actions across various reservoir ecosystems. Here, we investigated how the effect of the magnitude, frequency and duration of WLR on fish populations varies along environmental gradients. We used biomass, density, size, condition and maturation of brown trout (Salmo trutta L.) in Norwegian hydropower reservoirs as a measure of ecosystem response, and tested for interacting effects of WLR and lake morphometry, climatic conditions and fish community structure. Our results showed that environmental drivers modified the responses of brown trout populations to different WLR patterns. Specifically, brown trout biomass and density increased with WLR magnitude particularly in large and complex-shaped reservoirs, but the positive relationships were only evident in reservoirs with no other fish species. Moreover, increasing WLR frequency was associated with increased brown trout density but decreased condition of individuals within the populations. WLR duration had no significant impacts on brown trout, and the mean weight and maturation length of brown trout showed no significant response to any WLR metrics. Our study demonstrates that local environmental characteristics and the biotic community strongly modify the hydropower-induced WLR impacts on reservoir fishes and ecosystems, and that there are no one-size-fits-all solutions to mitigate environmental impacts. This knowledge is vital for sustainable planning, management and mitigation of hydropower operations that need to meet the increasing worldwide demand for both renewable energy and ecosystem services delivered by freshwaters.

Lightning talks presented by researchers from around the country that highlights the challenges they experienced with stakeholder engagement in FEWS projects and lessons learned from engagement. This presentation focused on engaging with stakeholders for prioritizing land and life in the Great Lakes region. It discussed important lessons for engaging with/by/as research partnerships. This includes prioritizing land and life by being thoughtful and intentional, being deliberate and make evident your goals, and use academic and scientific tools, methods and resources for protection, restoration and revitalization. Another important lesson is to understand your topic, project, stakeholders and self.

ReUse
License
CC-BY-NC-SA 4.0
Recommended Citation
Gagnon, V. (2021). Prioritizing land and life in the Great Lakes region. Northwest Knowledge Network (NKN) at the University of Idaho. https://doi.org/10.7923/8GHP-3125

Evaporation rates were measured at Lake Mead from March 2010 through February 2012 for phase 1 of an evaporation study (Moreo and Swancar, 2013). Phase 2 of the study (March 2012 through September 2017) continues evaporation measurements at Lake Mead and begins evaporation measurements at another lower Colorado River Basin reservoir, Lake Mohave. Eddy covariance is the primary measurement method. Data currently (10/6/2015) are being collected for the phase 2 study. This USGS data release represents tabular data in support of the evaporation study. The data release was produced in compliance with the new 'open data' requirements as way to make the scientific products associated with USGS research efforts and publications available to the public. The data release consists of 2 separate items: 1. Lake Mead evaporation data from March 2010 through April 2015 (Microsoft Excel workbook) 2. Lake Mohave evaporation data from May 2013 through April 2015 (Microsoft Excel workbook)

A regional assessment of thermokarst lakes across the Arctic Coastal Plain of Alaska was conducted using satellite images to detect changes in lake coverage and morphometry during the satellite era. This effort was supplemented by the use of digital aerial photographs to extend the analysis back to ~1950, and to assess temporal patterns of change. The analyses were augmented by summer field studies focused on lake evaporation, seasonal and interannual changes in fundamental lake characteristics, and collection of lake water temperature and bathymetry in three study areas. The measurement program is designed to map patterns of shoreline changes, monitor interannual variations in lake levels, and estimate energy and moisture exchange between lakes and the atmosphere. In summer 2008, lakes near Barrow were studied and instrumented. In 2009, the focus was on lakes further inland near Atqasuk. In 2010, lakes near the Arctic Coastal Plain-Arctic Foothills were studied.

These data provide the 2022 update of the Electricity Annual Technology Baseline (ATB). Starting in 2015 NREL has presented the ATB, consisting of detailed cost and performance data, both current and projected, for electricity generation and storage technologies. The ATB products now include data (Excel workbook, Tableau workbooks, and structured summary csv files), as well as documentation and user engagement via a website, presentation, and webinar. Starting in 2021, the data are cloud optimized and provided in the OEDI data lake. The data for 2015 - 2020 are can be found on the NREL Data Search Page. The website documentation can be found on the ATB Website.

Natural cover includes areas that have been mapped as woodlands (including plantations and hedgerows), wetlands and other rare vegetative cover communities.

Data here represent areas outlined in the Lake Simcoe Protection Plan Policy 6.48 (June 2011).

This product requires the use of GIS software.

*[GIS]: geographic information system