Accurate estimation of electricity consumption at multi-temporal resolutions is crucial for formulating safe and efficient energy management strategies. However, reliable data on daily and monthly urban electricity consumption is often limited. This study develops a top-down approach based on multi-source data to measure the daily and monthly electricity consumption at city-level. Using this method, we calculated the daily and monthly electricity consumption for 296 cities in China. Additionally, we explored the validity of the measurement results from multiple perspectives. This dataset is highly consistent with the officially released national-scale electricity consumption statistics, with a Pearson correlation coefficient of 0.8878. The dataset in this study can be used for analysis in a variety of cutting-edge research fields, such as urban power system resilience assessment, urban power system risk management strategy and policy development.

Description

The repository contains an extensive dataset of PV power measurements and a python package (qcpv) for quality controlling PV power measurements. The dataset features four years (2014-2017) of power measurements of 175 rooftop mounted residential PV systems located in Utrecht, the Netherlands. The power measurements have a 1-min resolution.

PV power measurements

Three different versions of the power measurements are included in three data-subsets in the repository. Unfiltered power measurements are enclosed in unfiltered_pv_power_measurements.csv. Filtered power measurements are included as filtered_pv_power_measurements_sc.csv and filtered_pv_power_measurements_ac.csv. The former dataset contains the quality controlled power measurements after running single system filters only, the latter dataset considers the output after running both single and across system filters. The metadata of the PV systems is added in metadata.csv. This file holds for each PV system a unique ID, start and end time of registered power measurements, estimated DC and AC capacity, tilt and azimuth angle, annual yield and mapped grids of the system location (north, south, west and east boundary).

Quality control routine

An open-source quality control routine that can be applied to filter erroneous PV power measurements is added to the repository in the form of the Python package qcpv (qcpv.py). Sample code to call and run the functions in the qcpv package is available as example.py.

Objective

By publishing the dataset we provide access to high quality PV power measurements that can be used for research experiments on several topics related to PV power and the integration of PV in the electricity grid.

By publishing the qcpv package we strive to set a next step into developing a standardized routine for quality control of PV power measurements. We hope to stimulate others to adopt and improve the routine of quality control and work towards a widely adopted standardized routine.

Data usage

If you use the data and/or python package in a published work please cite: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.

Units

Timestamps are in UTC (YYYY-MM-DD HH:MM:SS+00:00).

Power measurements are in Watt.

Installed capacities (DC and AC) are in Watt-peak.

Additional information

A detailed discussion of the data and qcpv package is presented in: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Corrections are discussed in: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2024. Erratum: Open-source quality control routine and multiyear power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.

Acknowledgements

This work is part of the Energy Intranets (NEAT: ESI-BiDa 647.003.002) project, which is funded by the Dutch Research Council NWO in the framework of the Energy Systems Integration & Big Data programme. The authors would especially like to thank the PV owners who volunteered to take part in the measurement campaign.

Detailed household load and solar generation in minutely to hourly resolution. This data package contains measured time series data for several small businesses and residential households relevant for household- or low-voltage-level power system modeling. The data includes solar power generation as well as electricity consumption (load) in a resolution up to single device consumption. The starting point for the time series, as well as data quality, varies between households, with gaps spanning from a few minutes to entire days. All measurement devices provided cumulative energy consumption/generation over time. Hence overall energy consumption/generation is retained, in case of data gaps due to communication problems. Measurements were conducted 1-minute intervals, with all data made available in an interpolated, uniform and regular time interval. All data gaps are either interpolated linearly, or filled with data of prior days. Additionally, data in 15 and 60-minute resolution is provided for compatibility with other time series data. Data processing is conducted in Jupyter Notebooks/Python/pandas.

Global electricity generation has increased significantly over the past three decades, rising from less than 12,000 terawatt-hours in 1990 to almost 30,000 terawatt-hours in 2023. During this period, electricity generation worldwide only registered an annual decline twice: in 2009, following the global financial crisis, and in 2020, amid the coronavirus pandemic. Sources of electricity generation The share of global electricity generated from clean energy sources –including renewables and nuclear power- amounted to almost 40 percent in 2023, up from approximately 32 percent at the beginning of the decade. Despite this growth, fossil fuels are still the main source of electricity generation worldwide. In 2023, almost 60 percent of the electricity was produced by coal and natural gas-fired plants. Regional differences Water, wind, and sun contribute to making Latin America and the Caribbean the region with the largest share of renewable electricity generated in the world. By comparison, several European countries rely on nuclear energy. However, the main electricity sources in the United States and China, the leading economic powers of the world, are respectively natural gas and coal.

Note: Find data at source. ・ This data hub, COVID-EMDA+ (Coronavirus Disease - Electricity Market Data Aggregation+), is specifically designed to track the potential impacts of COVID-19 on the existing U.S. electricity markets. Many different data sources are merged and harmonized here in order to enable further interdisciplinary researches. (https://github.com/tamu-engineering-research/COVID-EMDA)Publication: https://www.cell.com/joule/fulltext/S2542-4351(20)30398-6#%20This data hub contains five major components: U.S. electricity market data, public health data, weather data, mobile device location data, and satellite images. For some categories, multiple data sources are carefully gathered to ensure accuracy.Electricity Market Data includes the generation mix, metered load profiles and day-ahead locational marginal prices data. We also include the day-ahead load forecasting, congestion price, forced outage and renewable curtailment data as the supplementary source. (Link: CAISO, MISO, ISO-NE, NYISO, PJM, SPP, ERCOT, EIA, EnergyOnline) Public Health Data includes the COVID-19 confirmed cases, deaths data, infection rate and fatal rate. We aggregate and fine-tune the data to market and city levels. (Link: John Hopkins CSSE)

Weather Data includes temperature, relative humidity, wind speed and dew point data. Typical weather stations are selected according to their geological locations and data quality. (Link: Iowa State Univ IEM, NOAA)

Mobile Device Location Data includes social distancing data and patterns of visits to Point of Interests (POIs). These data are derived by aggregating and processing the real-time GPS location of cellphone users by Census Block Group. To obtain the access to the original data, please click the link below and apply for SafeGraph's permission (totally free). (Link: Mobility Data from SafeGraph)

Night Time Light (NTL) Satellite Data includes the raw satellite image taken at night time in each area. (Link: NTL Images from NASA) The original data sources for the COVID-EMDA+ data hub are listed at https://www.cell.com/cms/10.1016/j.joule.2020.08.017/attachment/a9f9c743-1252-41f4-bba3-913f3b01aa5a/mmc1.pdf.

Electricity Output Prediction Market Outlook

The global electricity output prediction market size was valued at approximately USD 1.2 billion in 2023 and is expected to grow at a robust CAGR of 15.8% from 2024 to 2032, reaching an estimated value of USD 4.5 billion by the end of the forecast period. This growth is propelled by the increasing demand for efficient energy management systems and the integration of renewable energy sources into the power grid.

One of the key growth factors driving the electricity output prediction market is the rising adoption of renewable energy sources. As countries worldwide strive to meet their renewable energy targets and reduce carbon emissions, the need for accurate electricity output prediction becomes crucial. Renewable energy sources such as solar and wind are inherently variable, and predicting their output accurately is essential for grid stability and efficient energy management. Consequently, energy providers and grid operators are increasingly investing in advanced predictive analytics tools to optimize the integration of renewables.

Another significant factor contributing to market growth is the advancement in predictive analytics technologies. The development and adoption of sophisticated algorithms, such as machine learning and neural networks, have significantly improved the accuracy of electricity output predictions. These technologies enable better analysis of historical data and real-time monitoring, allowing for more reliable and efficient energy management. Companies in the energy sector are increasingly recognizing the value of these advanced tools in improving operational efficiency, reducing costs, and enhancing decision-making processes.

The growing importance of smart grids and the digitization of energy infrastructure also play a pivotal role in the market's expansion. Smart grids enable better monitoring and management of electricity flow, and predictive analytics are integral to their operation. By leveraging data from various sources, including smart meters, weather forecasts, and historical consumption patterns, predictive models can provide accurate forecasts of electricity demand and supply. This helps in optimizing energy distribution, reducing wastage, and ensuring a stable and reliable power supply.

From a regional perspective, North America and Europe are expected to dominate the electricity output prediction market, owing to their advanced energy infrastructure and early adoption of renewable energy sources. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. Rapid industrialization, urbanization, and increasing investments in smart grid projects are driving the demand for electricity output prediction solutions in this region. Countries like China and India are particularly focusing on expanding their renewable energy capacity, further boosting market growth.

Component Analysis

The electricity output prediction market is segmented by components, including software, hardware, and services. The software segment holds the largest market share due to the increasing adoption of advanced predictive analytics tools. Software solutions for electricity output prediction encompass various applications, such as energy management systems, demand forecasting, and grid optimization. These software tools utilize advanced algorithms and machine learning techniques to analyze data from multiple sources and provide accurate predictions of electricity output.

In terms of hardware, the market is witnessing significant growth due to the rising implementation of smart meters, sensors, and other IoT devices. These hardware components are essential for collecting real-time data on electricity consumption, weather conditions, and other relevant factors. The integration of hardware with predictive analytics software enhances the accuracy and reliability of electricity output predictions. As smart grid projects continue to expand globally, the demand for advanced hardware solutions is expected to rise.

The services segment, which includes consulting, implementation, and maintenance services, is also experiencing steady growth. Energy providers, grid operators, and industrial users often require expert guidance and support to effectively implement and utilize electricity output prediction solutions. Service providers offer customized solutions tailored to the specific needs of their clients, ensuring optimal performance and efficiency. As the complexity of energy systems increases, the demand for professional services

Anomaly lists are presented documenting operational interference to electricity power grids and communication networks in the United States and Canada during magnetic storms. Four of the anomaly lists apply for magnetic storms that occurred in March 1989, August 1972, March 1940, and for various storms 1946-2000; yet another list consists of statistical values summarizing geomagnetically induced current data for 1969-1972. The lists are compiled from source published papers, technical documents, and research papers. These sources generally include brief descriptions of each anomaly and attribution to a particular magnetic storm. Other information, when given, includes utility company name, facility name, start date and time, end date and time. None of the sources include specific locations (latitude and longitude) of the anomalies. In the lists given here, the latitude and longitude of each anomaly are obtained either from a list of power-grid facilities available from the Department of Homeland Security, by estimating facility locations from digitized and georeferenced paper maps, or from internet-based maps.

Introduction: With the increasing fluctuations in the current domestic and international economic situation and the rapid iteration of macroeconomic regulation and control demands, the inadequacy of the existing economic data statistical system in terms of agility has been exposed. It has become a primary task to closely track and accurately predict the domestic and international economic situation using effective tools and measures to compensate for the inadequate economic early warning system and promote stable and orderly industrial production.Methods: Against this background, this paper takes industrial added value as the forecasting object, uses electricity consumption to predict industrial added value, selects factors influencing industrial added value based on grounded theory, and constructs a big data forecasting model using a combination of “expert interviews + big data technology” for economic forecasting.Results: The forecasting accuracy on four provincial companies has reached over 90%.Discussion: The final forecast results can be submitted to government departments to provide suggestions for guiding macroeconomic development.

Spatial extent of existing and planned electricity grid. A new electricity grid layer was compiled by using multiple sources that enumerates elements of the existing transmission and distribution network. These sources include Open Street Map, the Word Bank datasets, Arderne et al., the Economic Community of West African States Observatory for Renewable Energy and Energy Efficiency, and from rural electrification agencies/EU delegations in Africa (Burkina Faso, Kenya, Tanzania).

This dataset is restricted, for more information please contact the author. Data were collected from multiple sources:The Electricity & Co-Generation Regulatory AuthoritySaudi Electricity companyWeb news article (2015, December 28). Increase of Fuel, Electricity and Water prices. Retrieved from https://akhbaar24.argaam.com/article/detail/255091accessed on March 22, 2018.In October 1984, the government adopted a Tariff that increased with increasing consumption. The changes of Tariffs started in November 1984.Tariff approved by Council of Ministries 170 and become effective in October 2000. This Tariff remained effective for approximately ten years The residential, agricultural, mosques, and charitable societies remained unchanged till 2018In 2010, a new tariff for government, commercial, and industrial consumption came into force, this was adopted by a decision of ECRA's board, to set tariffs for non-residential consumption with an upper limit of SR0.26/kWh.In 2015, the total value of electricity consumed by the residential sector was worth about 38 billion U.S. dollars.In 2018, the Council of Ministers has approved gradual revision of energy prices in the Kingdom including changes to electricity tariffs effective from Jan. 1. 2018, the Electricity and Cogeneration Regulatory Authority (ECRA) announced that new prices will take effect on January 1st, 2018.source: ECRACitation: Alghamdi, Abeer. 2018. “Changes in Saudi Arabia Electricity Prices.” [dataset]. https://datasource.kapsarc.org/explore/dataset/electricity-prices-in-saudi-arabia/information/.

Over the past half a century, the world's electricity consumption has continuously grown, reaching approximately 27,000 terawatt-hours by 2023. Between 1980 and 2023, electricity consumption more than tripled, while the global population reached eight billion people. Growth in industrialization and electricity access across the globe have further boosted electricity demand. China's economic rise and growth in global power use Since 2000, China's GDP has recorded an astonishing 15-fold increase, turning it into the second-largest global economy, behind only the United States. To fuel the development of its billion-strong population and various manufacturing industries, China requires more energy than any other country. As a result, it has become the largest electricity consumer in the world. Electricity consumption per capita In terms of per capita electricity consumption, China and other BRIC countries are still vastly outpaced by developed economies with smaller population sizes. Iceland, with a population of less than half a million inhabitants, consumes by far the most electricity per person in the world. Norway, Qatar, Canada, and the United States also have among the highest consumption rates. Multiple contributing factors such as the existence of power-intensive industries, household sizes, living situations, appliance and efficiency standards, and access to alternative heating fuels determine the amount of electricity the average person requires in each country.

egon-data provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project eGoⁿ. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGoⁿ project website or its Github repository.

egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data.

The following data sets are part of the available data bundle:

1. climate_zones_germany
   • Climate zones in Germany
   • source: Own representation based on DWD TRY climate zones
   • License: Attribution 4.0 International (CC BY 4.0)
2. emobility
   • Data on eMobility mit_trip_data:
     motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR).
   • Reiner Lemoine Institut, June 2022
   • License: Attribution 4.0 International (CC BY 4.0)
3. geothermal_potential
   • Spatial distribution of deep geothermal potentials in Germany
   • source: Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook
   • License: Attribution 4.0 International (CC BY 4.0)
4. household_electricity_demand_profiles
   • Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children.
     The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012.
     The columns are named as follows: "
   • License: Attribution 4.0 International (CC BY 4.0)
5. household_heat_demand_profiles
   • Sample heat time series including hot water and space heating for single- and multi-familiy houses. The profiles were created using the loadprofile generator by Fraunhofer IEE developed in the Master's thesis "Synthesis of a heat and electrical load profile for single and multi-family houses used for subsequent performance tests of a multi-component energy system", Simon Ruben Drauz, RWTH Aachen University, March 2016
   • License: Attribution 4.0 International (CC BY 4.0)
6. hydrogen_storage_potential_saltstructures
   • The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..
   • Source: Flach lagernde Salze, (c) BGR Hannover, 2021.
     Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. &
     Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., &
     Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und
     Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen,
     Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen
     zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) –
     Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht.
     Hannover: BGR.
   • License: The original data are licensed under the GeoNutzV, see https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf
7. industrial_sites
   • Information about industrial sites with DSM-potential in Germany from a Master's thesis by Danielle Schmidt. The data set includes own information on the coordinates of every industrial site.
   • source: Schmidt, Danielle. (2019). Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3613767
   • License: Attribution 4.0 International (CC BY 4.0)
8. nep2035_version2021
   • Data extracted from the German grid development plan - power
   • source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0
   • License: Attribution 4.0 International (CC BY 4.0)
9. pipeline_classification_gas
   • Parameters for the classification of gas pipelines
   • source: Single parameters extracted from Electricity, Heat and Gas Sector Data for Modelling the German System
   • License: Attribution 4.0 International (CC BY 4.0)
10. pypsa_eur_sec
    • Preliminary results from scenario generator pypsa-eur-sec
    • source: own calculation using pypsa-eur-sec fork (https://github.com/openego/pypsa-eur-sec)
    • License: Attribution 4.0 International (CC BY 4.0)
11. regions_dynamic_line_rating
    • German regions suitable to model dynamic line rating
    • source: Own representation based on Grundsätze für die Ausbauplanung des Deutschen Übertragungsnetze (2020)
    • License: Attribution 4.0 International (CC BY 4.0)
12. re_potential_areas
    • Eligible areas for wind turbines and ground-mounted PV systems.
    • Reiner Lemoine Institut, January 2022
    • License: Attribution 4.0 International (CC BY 4.0)
13. WZ_definition
    • Definitions of industrial and commercial branches
    • source: Klassifikation der Wirtschaftszweige (WZ 2008)
    • Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.
14. zensus_households
    • Dataset describing the amount of people living by a certain types of family-types, age-classes,sex and size of household in Germany in state-resolution.
    • source: Data retrieved from Zensus Datenbank by performing these steps:
      • Search for: "1000A-2029"
      • or choose topic: "Bevölkerung kompakt"
      • Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"
      • Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.
    • Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.

Contains the model version of Euro-Calliope applied in the multi-model analysis "Open Source Energiewende" and the aggregated results of six scenarios. See ./README.md for more information.

The Singapore data center power market, valued at $1.61 billion in 2025, is projected to experience steady growth, driven by the burgeoning digital economy and increasing cloud adoption. A Compound Annual Growth Rate (CAGR) of 3.20% from 2025 to 2033 indicates a robust expansion, fueled by the nation's strategic focus on becoming a leading digital hub in Southeast Asia. Key drivers include the rising demand for high-availability power solutions, stringent regulatory requirements for data center uptime, and the proliferation of 5G networks and IoT devices, all contributing to higher energy consumption. The market is segmented by power infrastructure solutions (UPS systems, generators, power distribution solutions), services (maintenance, installation, consulting), and end-users (IT & Telecommunications, BFSI, Government, Media & Entertainment). Major players like ABB, Schneider Electric, and Vertiv are actively competing, offering a diverse range of solutions tailored to specific data center needs. The increasing adoption of energy-efficient technologies and renewable energy sources within data centers will shape future market dynamics, presenting both opportunities and challenges for existing and emerging players. The forecast period (2025-2033) anticipates consistent growth, influenced by government initiatives promoting digital transformation and investments in advanced infrastructure. However, potential restraints include the high initial investment costs associated with upgrading power infrastructure and the need for skilled manpower to manage complex data center power systems. To mitigate these challenges, data center operators are increasingly adopting hybrid power solutions combining traditional and renewable sources, optimizing energy efficiency, and leveraging advanced power management systems for enhanced reliability and reduced operational costs. This focus on sustainability and efficiency will be a significant factor in shaping the future landscape of the Singapore data center power market, attracting further investment and technological advancements. This in-depth report provides a comprehensive analysis of the Singapore data center power market, offering invaluable insights into market size, growth drivers, challenges, and future trends. Covering the period from 2019 to 2033, with a base year of 2025 and a forecast period spanning 2025-2033, this study is an essential resource for businesses operating in or planning to enter this dynamic sector. The report delves into market segmentation, competitor analysis, and key industry developments, providing a detailed understanding of this crucial aspect of Singapore's digital infrastructure. Expect detailed analysis of UPS systems, generators, power distribution solutions, and hydrogen fuel cells, among others. This report is designed to maximize search visibility for keywords like "Singapore data center market," "data center power infrastructure Singapore," "Singapore data center power consumption," and "data center energy solutions Singapore." Recent developments include: January 2024: Caterpillar Inc. partnered with Microsoft and Ballard Power Systems to test the use of large-format hydrogen fuel cells as a reliable and eco-friendly backup power source for multi-megawatt data centers. Hydrogen fuel cells are seen as a possible low-carbon alternative to diesel backup generators, which is expected to drive the growth of DC generators., March 2024: Schneider Electric announced the expansion of its US manufacturing facilities at two locations to support critical infrastructure of data centers and other industries. At both locations, the company planned to manufacture electrical switchgear and medium-voltage power distribution products.. Key drivers for this market are: The Rising Adoption of Mega Data Centers and Cloud Computing, Increasing Demand to Reduce Operational Costs. Potential restraints include: High Cost of Installation and Maintenance. Notable trends are: The IT and Telecom Segment is Expected to Maintain a Significant Market Share.

and therefore

With the development of the smart grid and energy Internet, the power industry generates huge, multi-source, heterogeneous, and highly coupled data, which are difficult to utilize. The intelligent operation and maintenance system of the power transformer based on the knowledge graph and graph neural network is developed in this article. The multi-source heterogeneous data are structured and modeled by the constructed knowledge graph, and it presents the correlation among data more intuitively. On this basis, the graph neural network is designed to achieve the prediction and excavate the deep information hidden in the data. The testing results show that the system has fully used the multi-dimensional and interrelated heterogeneous data, achieving a deep information mine. It benefits the management and strategy implementation for the system scientifically and guides the operation and maintenance of the transformer. The system is of great significance on improving the efficiency of the transformer maintenance and safe operation.

The France data center power market, valued at approximately €713.90 million in 2025, is projected to experience steady growth, driven by the increasing adoption of cloud computing, big data analytics, and the expanding digital economy. This growth is expected to continue at a Compound Annual Growth Rate (CAGR) of 2.87% from 2025 to 2033, reaching a substantial market size by the end of the forecast period. Key drivers include the rising demand for high-availability power solutions within data centers to ensure business continuity and minimize downtime, stringent regulatory requirements for energy efficiency, and the escalating need for robust power infrastructure to support the growing IT and telecommunications sector in France. The market is segmented by power infrastructure (UPS systems, generators, power distribution solutions) and end-user sectors (IT & Telecommunication, BFSI, Government, Media & Entertainment). Major players like ABB, Schneider Electric, and Vertiv are actively contributing to market expansion through technological advancements and strategic partnerships. The market's growth is also influenced by trends such as the increasing adoption of renewable energy sources within data centers to reduce carbon footprint and improve sustainability, along with the growing popularity of modular data center designs, offering flexibility and scalability. However, factors such as high initial investment costs associated with advanced power infrastructure and potential regulatory hurdles may act as restraints to some extent. Despite these restraints, the long-term outlook for the France data center power market remains positive, fuelled by the continuous expansion of data center facilities and increased investments in digital infrastructure across various sectors. The market is expected to be characterized by intense competition, with established players and emerging technology providers striving for market share through product innovation, service enhancements, and strategic acquisitions. Recent developments include: January 2024: Caterpillar Inc. partnered with Microsoft and Ballard Power Systems to test the use of large-format hydrogen fuel cells as a reliable and eco-friendly backup power source for multi-megawatt data centers. Hydrogen fuel cells are seen as a possible low-carbon alternative to diesel backup generators, which is expected to drive the growth of DC generators., March 2024: Schneider Electric announced the expansion of US manufacturing facilities at two locations to support critical infrastructure of data centers and other industries. At both locations, the company planned to manufacture electrical switchgear and medium-voltage power distribution products.. Key drivers for this market are: Rising Adoption of Mega Data Centers and Cloud Computing, Increasing Demand to Reduce Operational Costs. Potential restraints include: Rising Adoption of Mega Data Centers and Cloud Computing, Increasing Demand to Reduce Operational Costs. Notable trends are: IT & Telecommunication Segment Holds the Major Share.

1. The AuDP algorithm does not require any hyperparameter adjustment and can meet various constraints. You can compute the optimal control results under different gas purge time constraints by adjusting the variable [OFF2ON] below. 
2. The battery model integrates the SOP algorithm recommended by the battery supplier and includes a simple battery thermal model. However, we currently do not have permission to disclose the SOP data, so parts of the code involving battery data have been encapsulated. The open-source code is sufficient to demonstrate most of the details of AuDP. In the future, we will seek publicly available SOP data and open-source all the code. /*You can find the SOP algorithm details in [+Pseudocode/Cal_SOC].*/ 
3. It is recommended to read the AuDP files in the following order: +AuDP_Function/AuDP.m +AuDP_Function/AuDP_Single_Step.m +AuDP_Function/Mode_Masking.m +AuDP_Function/Start_Mode.m +AuDP_Function/Work_Mode.m +AuDP_Function/Close_Mode.m +AuDP_Function/EV_Mode.m +AuDP_Function/SOC_Masking.m +AuDP_Function/Data_Merge.m +AuDP_Function/Result_Display.m 
4. Running [main.m] to get the result of the calculation. You can also directly read the data in [+Data_Storage] to view the calculation results, and use [+AuDP_Function/Result_Display] to visualize the data. 
5. This code was developed using Matlab 2022b, so it is recommended to run it in Matlab 2022b to avoid potential errors. On a laptop configured with a 12th Gen Intel(R) Core(TM) i9-12900HX 2.30 GHz CPU, 32.0 GB RAM, and RTX3080Ti, the runtime of this code is approximately 185–195 seconds. Note that different Matlab versions and laptop configurations may result in varying computation times, which is normal.

If you use or refer to this code, it is recommended that you cite our paper: Wang KY, Song H, Guo ZQ, Zhang XM. Automated multi-dimensional dynamic planning algorithm for solving energy management problems in fuel cell electric vehicles. Energy 2025;316:134408. https://doi.org/10.1016/j.energy.2025.134408

Residential electricity prices data for Saudi Arabia, UAE, Bahrain, Oman and Kuwait collected from multiple sources. Saudi Arabia electricity tariffs: KAPSARC dataOman: Authority for Electricity Regulations - Link 2019 Annual Report Bahrain: Electricity & Water Authority - Link - ​​​​​​​​​​​​​​​​​​​​​Electricity Consumption Tariff for the years 2016-2019UAE electricity prices: Dubai: Dubai Electricity & Water Authority - Link Sharjah: Sharjah Electricity & Water Authority - Link Access Abu Dhabi prices dataset Link, Source: Abu Dhabi Distribution Company - Link Water & Electricity Tariffs 2017Other emirates in UAE: Federal Electricity & Water Authority - Link Global average price - link World average price is 0.14 U.S. Dollar per kWh for household users and 0.13 U.S. Dollar per kWh for business users.Note: Global average price for world countries include all items in the electricity bill such as the distribution and energy cost, various environmental and fuel cost charges and taxes.All prices are converted to (US cent/KWh). Citation: Alghamdi, Abeer. 2020. “GCC Residential Electricity Tariffs.” [dataset]. https://datasource.kapsarc.org/explore/dataset/gcc-electricity/information/?disjunctive.country_city&disjunctive.category&disjunctive.slabs.

The Austria Data Center Power market, valued at approximately €88.10 million in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 8.19% from 2025 to 2033. This expansion is fueled by several key drivers. Firstly, the increasing adoption of cloud computing and digital transformation initiatives across various sectors, including IT & Telecommunications, BFSI (Banking, Financial Services, and Insurance), and the Government, are significantly boosting demand for reliable and efficient power solutions within data centers. Secondly, the growing focus on ensuring business continuity and minimizing downtime through resilient power infrastructure is driving investment in advanced power systems like UPS, generators, and critical power distribution solutions. Furthermore, stringent regulatory compliance requirements related to data security and energy efficiency are further propelling market growth. The market segmentation reveals a strong presence of power infrastructure solutions (UPS systems, generators, power distribution solutions), services supporting these systems, and a diverse end-user base. Key players like ABB Ltd, Eaton Corporation, and Schneider Electric are actively shaping the market landscape through technological advancements and strategic partnerships. However, challenges remain. The high initial investment associated with implementing sophisticated power infrastructure can hinder adoption, particularly among smaller data centers. Furthermore, potential disruptions in the global supply chain for critical components and skilled labor shortages could impact market growth. Despite these restraints, the long-term outlook for the Austria Data Center Power market remains positive, driven by the unstoppable trend toward digitalization and the increasing reliance on robust data center infrastructure. The market is poised to benefit from continuous innovation in power management technologies, improved energy efficiency, and the expanding adoption of sustainable power solutions, leading to further growth in the coming years. Specific regional variations within Austria, while not explicitly provided, would likely mirror national trends, reflecting varying levels of digital adoption across different regions. Recent developments include: • January 2024: Caterpillar Inc. partnered with Microsoft and Ballard Power Systems to test the use of large-format hydrogen fuel cells as a reliable and eco-friendly backup power source for multi-megawatt data centers. Hydrogen fuel cells are seen as a possible low-carbon alternative to diesel backup generators, which is expected to drive the growth of DC generators., • March 2024: Schneider Electric announced its expansion of US manufacturing facilities at two locations to support critical infrastructure of data centers and other industries. The company planned to manufacture electrical switchgear and medium-voltage power distribution products at both locations.. Key drivers for this market are: Rising Adoption of Mega Data Centers and Cloud Computing, Increasing Demand to Reduce Operational Costs. Potential restraints include: Rising Adoption of Mega Data Centers and Cloud Computing, Increasing Demand to Reduce Operational Costs. Notable trends are: IT & Telecommunication Segment Holds the Major Share.