Description for Solar Power Generation

UNISOLAR dataset contains high-granularity Photovoltaic (PV) solar energy generation, solar irradiance, and weather data from 42 PV sites deployed across five campuses at La Trobe University, Victoria, Australia. The dataset includes approximately two years of PV solar energy generation data collected at 15-minute intervals. Geographical placement and engineering specifications for each of the sites are also provided to aid researchers in modeling solar energy generation. Weather data is available at 1-minute intervals and is provided by the Australian Bureau of Meteorology (BOM). Apparent temperature, air temperature, dew point temperature, relative humidity, wind speed, and wind direction were provided under the weather data. The paper describes the data collection. methods, cleaning, and merging with weather data. This dataset can be used to forecast, benchmark, and enhance operational outcomes in solar sites.

Acknowledgements Please cite the following paper if you use this dataset:

S. Wimalaratne, D. Haputhanthri, S. Kahawala, G. Gamage, D. Alahakoon and A. Jennings, "UNISOLAR: An Open Dataset of Photovoltaic Solar Energy Generation in a Large Multi-Campus University Setting," 2022 15th International Conference on Human System Interaction (HSI), 2022, pp. 1-5, doi: 10.1109/HSI55341.2022.9869474. Usage Policy and Legal Disclaimer This dataset is being distributed only for Research purposes, under Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By clicking on download button(s) below, you are agreeing to use this data only for non-commercial, research, or academic applications. You may need to cite the above papers if you use this dataset.

Github: https://github.com/CDAC-lab/UNISOLAR

Description for Power Consumption Data

About Dataset

UNICON, a large-scale open dataset on University Consumption of utilities, electricity, gas and water. This dataset is publicly released as part of La Trobe University’s commitment to Net Zero Carbon Emissions by 2029, for which we are building the La Trobe Energy AI/Analytics Platform (LEAP) that leverages Artificial Intelligence (AI) and Data Analytics to analyze, predict and optimize the consumption, generation and utilization of electricity, renewables, gas and water resources. UNICON contains consumption data for La Trobe’s five campuses in geographically distributed regions, across four years, 2018-2021 inclusive. This includes the COVID-19 global pandemic timeline of university shutdown and work from home measures that led to a significant decrease in the consumption of utilities. The consumption data consists of smart electricity meter readings at 15-minute granularity, gas meter readings at hourly intervals and water meter readings at 15-minute intervals. UNICON also contains weather data from the closest weather station to each campus, collected at two-speed latency of 1 minute and 10 minutes. The dataset is annotated with internal events of significance, such as energy conservation measures (ECMs) and other measurement and validation (M&V) activities conducted as part of LEAP optimization. To the best of our knowledge, this is the first large-scale, comprehensive, open dataset for the three main utilities, electricity, gas, and water consumption in a multi-campus university setting.

Dataset file descriptions

campus_meta.csv – This file contains information about each campus in the university network.

nmi_meta.csv – Information about NMIs such as campus location and peak demand is listed in this file.

building_meta.csv – This file contains meta information about buildings in each campus which include campus location, floor area and etc.

calender.csv – University calendar for the data collection period is included in this file.

events.csv – There are series of events happened at each building which include energy efficiency projects such as LED installation and HVAC system updates. This file contains the dates related to each event at building level.

nmi_consumption.csv – Consumption data of NMIs are recorded in this file.

building_consumption.csv – Consumption data of buildings are recorded in this file.

building_submeter_consumption.csv – Consumption data of building sub-meters are recorded in this file.

gas_consumption.csv – Gas consumption data of available campuses are recorded in this file.

water_consumption.csv – Water consumption data of available campuses are recorded in this file.

weather_data.csv – Weather data collected from respective weather stations.

Acknowledgements Please cite the following paper if you use this dataset:

H. Moraliyage, N. Mills, P. Rathnayake, D. De Silva and A. Jennings, "UNICON: An Open Dataset of Electricity, Gas and Water Consumption in a Large Multi-Campus University Setting," 2022 15th International Conference on Hu...

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.

Solar home half-hour data and Solar home monthly data.

The half-hour electricity data is for 300 homes with rooftop solar systems that are measured by a gross meter that records the total amount of solar power generated every 30 minutes.

The monthly electricity data is for 2,657 solar homes with rooftop solar systems that have a gross metering configuration.

The design of this 1,280-square-foot, three-bedroom Habitat for Humanity of Metro Denver zero energy home carefully combines envelope efficiency, efficient equipment, appliances and lighting, and passive and active solar features to reach the zero energy goal. The home was designed with an early version (July 22, 2004) of the BEOpt building optimization software; DOE2 and TRNSYS were used to perform additional analysis. This engineering approach was tempered by regular discussions with Habitat construction staff and volunteers. These discussions weighed the applicability of the optimized solutions to the special needs and economics of a Habitat house, moving the design toward simple, easily maintained mechanical systems and volunteer-friendly construction techniques. A data acquisition system was installed in the completed home to monitor its performance. The home's energy performance was monitored for 10 years. A comprehensive report on the home was done after the first 2 years of monitoring and a follow-up ACEEE paper on the project including 10 years of performance data was presented in 2016.

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022).

These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data.

Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period.

Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented.

Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region.

One major issue with the residential profiles is that for each of the five climate zones, certain location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold).

The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these HIGH and LOW permutations represent the range of energy use in the housing stock.

Note that on July 2nd, 2013, the Residential High and Low load files were updated from 366 days in a year for leap years to the more general 365 days in a normal year. The archived residential load data is included from prior to this date.

Through the Residential Solar Investment Program (RSIP), the Connecticut Green Bank, in partnership with a network of contractors and inspectors, helped more than 46,300 households access solar energy since 2012, surpassing the statutory target of 350 MW (reaching 378 MW) one year ahead of the December 2022 deadline.

Highlights - $1.43B total investment - $156M total incentive - $0.41/W average incentive - $3.79/W installed cost

How it works - RSIP provided rebates and incentives to make rooftop solar more affordable for homeowners. - When panels produce electricity, they save money and create Solar Home Renewable Energy Credits (SHRECs). - Utilities entered into Master Purchase Agreements (MPAs) with the Green bank to buy SHRECs to comply with policy programs. - Green bonds are created via SHREC revenue and purchased by both individual and institutional buyers. - Revenue from MPAs and Green Bonds support RSIP incentives and cover administrative costs.

This dataset is an anonymized comprehensive listing of all approved and completed projects supported by the RSIP. It contains information on system costs, sizes, participating contractors, and other details gathered during the RSIP. More information and program evaluation reports can be found at https://www.ctgreenbank.com/strategy-impact/societal-impact/successful-legacy-programs/residential-solar-investment-program-rsip

This data contains the results of surveys given to Low Income Home Energy Assistance Program (LIHEAP)/Weatherization Assistance Program (WAP) administrators in 2022, 2023, and 2024, as well as spreadsheets coding states' annual LIHEAP and WAP plans from 2021, 2022, and 2023 for mentions of solar energy. This data is the second version of the original dataset which has been updated to include the most recent years of LIHEAP and WAP plans. The original version of the dataset is also part of this record but has been deprecated.

Here are a few use cases for this project:

1. Solar Panel Maintenance: The model could be used by solar panel service providers to automate the process of assessment and maintenance. By analyzing the state of the panels (clean, unclean, or dusty) it can help them identify which panels need immediate cleaning or service.

2. Industrial Inspection: In facilities with a large number of solar panels such as solar farms, the model could assist in streamlining routine checks. Rather than manual inspection, images can be taken and analyzed for cleanliness, helping to efficiently allocate cleaning resources and maintain optimum efficiency.

3. Home Automation Systems: The model could be integrated into smart home systems to alert homeowners when their solar panels are dirty or dusty. It can act as a smart tool for homes using solar energy as one of their primary energy sources.

4. Drone-based Inspection: For large scale solar installations in hard-to-reach areas (e.g. large roofs, deserts), drones equipped with cameras and the computer vision model can perform inspections. This can be safer and more effective, with the AI determining the status of each panel.

5. Educational Purposes: This computer vision model could be used as a teaching tool in educational institutions for courses related to renewable energy. It can demonstrate the importance of solar panel cleanliness in energy efficiency, encouraging students to engage with practical, real-world issues in their learning.

This data set is time series electricity use data from rural households using off-grid energy systems in Kenya. As well as indicating lighting electricity use for a real-world use case, it can give insight into active occupancy times in the mornings and evenings. This can support estimation of load profiles for higher tiers of the Multi-tier Framework for energy access by adding in load profiles for additional appliances.

Two solar nano-grids (SONGs) were built in two rural communities in Kenya, as part of the Solar Nano-grids project (EPSRC ref: EP/L002612/1). One aspect of the SONGs were battery-charging systems, in which batteries could be charged at a central solar hub, and used in households to power lighting and mobile phone charging. For each battery the electricity use was recorded in real-time between July 2016 and November 2016 inclusive.

The data consist of separate demand (use of battery in the home for lighting) and charging (charging at the central hub) profiles in csv files, individually for each household. The data are half-hourly measurements of average power used for the household lighting system (3 3W LED bulbs with wiring and switches). There is data for 51 households, ranging in length from 3 days to 5 months. Note that the data set is solely electricity use for the household lighting system, and does not include electricity use via the USB port that was present for charging mobile phones. The households are anonymised and are numbered in order of ascending number of days of data.

The household battery packs were Li-ion with capacity 62 Wh, and the data were recorded using a FRDM K-64F mbed embedded in each. 13 post-processing steps were required to process the data gathered in raw form from the batteries into energy profiles for individual households (see reference below). These included: correcting the timestamps caused by time drift or recalibration of the RTCs, attributing batteries to the correct household, addressing logging disruptions and inconsistent logging frequencies, imposing limits on power and duration of use to remove non-representative battery use, and testing loading conditions to remove abnormal energy use. The gaps in the data and varying lengths of the data are caused by: technical challenges with the batteries, meaning that they required frequent repairing; issues with the RTC on the microcontroller being reset; difficulty in attributing data to the correct household. Between 18th July - 1st August (approx.), the charging hub was shut down and so there is a gap in all energy profiles.

Graphical representations of the data for each household, and further information about the solar nano-grids project, the energy data, and the processing steps involved, can be found in Clements, A F. Data-driven approaches enabling the design of community energy systems in the Global South. DPhil Thesis. Department of Engineering Science, University of Oxford. 2019.

LIGHT dataset sample is provided for a month (2018/01). It consists of Generation (energy/kWh - 15min intervals) for photovoltaic array and battery storage, household power consumption (power/kW - 1min intervals) and photovoltaic array power (power/kW - 1min intervals).

Here are a few use cases for this project:

1. Solar Energy Research: Researchers could use the "merged_data" model to automatically identify and analyze placement of solar panels in aerial or street-view images. This could help understand trends in solar power adoption and optimization of panel placement with respect to trees and building structures.

2. Urban Planning: City or county urban planners could use the model to identify installations of solar panels, the intrusion of trees, and the presence of shadows on buildings. Such data will be valuable for future urban development planning, considering renewable energy sources, vegetation, and the optimization of natural light.

3. Renewable Energy Promotion: Government agencies or NGOs could use the model to scan neighborhoods and identify houses or buildings with solar panels and those without. They could then target the buildings without solar panels for promotion of solar energy benefits and subsidies.

4. Real Estate Market Analysis: Real estate agencies could use the model to identify properties with solar arrays to potentially value them higher due to their energy efficiency. They could also provide insights about possible renovations on properties for higher resale value like adding solar panels or cutting obstructing trees.

5. Energy Efficiency Rating: Energy rating agencies can use this model to identify the presence of solar panels, window size, and obstructions like trees or chimneys that may affect a building's energy efficiency. This information can be used to develop a more accurate energy efficiency rating for the building.

This dataset represents a complete European energy community based on actual data. In this scenario, a community of 250 households was built using real energy consumption and solar generation data obtained in homes throughout Europe. In total, 200 community members were assigned solar generation, while 150 were assigned a battery storage system. From the acquired sample, new profiles were created and randomly assigned to each end-user while also receiving two electric cars with information on their capacity, state-of-charge, and usage. Furthermore, it is provided the electric vehicle chargers’ information on their location, type, and cost of operation.

Version 1.5 update: on the Sheet EVs, lines 29 (Capacity kW), 30 (Charge kW), and 31 (Discharge kW) were updated to the correct values.

This work has been published in Elsevier's Data in Brief journal: Ricardo Faia, Calvin Goncalves, Luis Gomes, Zita Vale Dataset of an energy community with prosumer consumption, photovoltaic generation, battery storage, and electric vehicles Data in Brief, 2023, 109218, ISSN 2352-3409 https://doi.org/10.1016/j.dib.2023.109218 (https://www.sciencedirect.com/science/article/pii/S2352340923003372)

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.

Reference data used to create this dataset:

Filtered energy profiles and renewable energy production profiles: https://zenodo.org/record/6778401

Battery storage systems and electric vehicles: https://zenodo.org/record/4737293

his 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.

Acknowlegement

Foto von Thomas Kelley auf Unsplash

Field documentation

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household_data_1min_singleindex.csv

• utc_timestamp
  • Type: datetime
  • Format: fmt:%Y-%m-%dT%H%M%SZ
  • Description: Start of timeperiod in Coordinated Universal Time
• cet_cest_timestamp
  • Type: datetime
  • Format: fmt:%Y-%m-%dT%H%M%S%z
  • Description: Start of timeperiod in Central European (Summer-) Time
• interpolated
  • Type: string
  • Description: marker to indicate which columns are missing data in source data and has been interpolated (e.g. DE_KN_Residential1_grid_import;)
• DE_KN_industrial1_grid_import
  • Type: number (float)
  • Description: Energy imported from the public grid in a industrial warehouse building in kWh
• DE_KN_industrial1_pv_1
  • Type: number (float)
  • Description: Total Photovoltaic energy generation in a industrial warehouse building in kWh
• DE_KN_industrial1_pv_2
  • Type: number (float)
  • Description: Total Photovoltaic energy generation in a industrial warehouse building in kWh
• DE_KN_industrial2_grid_import
  • Type: number (float)
  • Description: Energy imported from the public grid in a industrial building of a business in the crafts sector in kWh
• DE_KN_industrial2_pv
  • Type: number (float)
  • Description: Total Photovoltaic energy generation in a industrial building of a business in the crafts sector in kWh
• DE_KN_industrial2_storage_charge
  • Type: number (float)
  • Description: Battery charging energy in a industrial building of a business in the crafts sector in kWh
• DE_KN_industrial2_storage_decharge
  • Type: number (float)
  • Description: Energy in kWh
• DE_KN_industrial3_area_offices
  • Type: number (float)
  • Description: Energy consumption of an area, consisting of several smaller loads, in a industrial building, part of a research institute in kWh
• DE_KN_industrial3_area_room_1
  • Type: number (float)
  • Description: Energy consumption of an area, consisting of several smaller loads, in a industrial building, part of a research institute in kWh
• DE_KN_industrial3_area_room_2
  • Type: number (float)
  • Description: Energy consumption of an area, consisting of several smaller loads, in a industrial building, part of a research institute in kWh
• DE_KN_industrial3_area_room_3
  • Type: number (float)
  • Description: Energy consumption of an area, consisting of several smaller loads, in a industrial building, part of a research institute in kWh
• DE_KN_industrial3_area_room_4
  • Type: number (float)
  • Description: Energy consumption of an area, consisting of several smaller loads, in a industrial building, part of a research institute in kWh
• DE_KN_industrial3_compressor
  • Type: number (float)
  • Description: Compressor energy consumption in a industrial building, part of a research institute in kWh
• DE_KN_industrial3_cooling_aggregate
  • Type: number (float)
  • Description: Cooling aggregate energy consumption in a industrial building, part of a research institute in kWh
• DE_KN_industrial3_cooling_pumps
  • Type: number (float)
  • Description: Cooling pumps energy consumption in a industrial building, part of a research institute in kWh
• DE_KN_industrial3_dishwasher
  • Type: number (float)
  • Description: Dishwasher energy consumption in a industrial building, part of a research institute in kWh
• DE_KN_industrial3_ev
  • Type: number (float)
  • Description: Electric Ve...

Here are a few use cases for this project:

1. Solar Energy Analysis: This model could be used to estimate the coverage of solar energy production in different regions. By identifying the number of houses or buildings using solar arrays, energy researchers and companies could make more accurate projections and plans.

2. Urban Planning: City and regional planners could use the SolarDetector_journal to assess renewable energy adoption in urban/rural areas. This could help promote the benefits of solar power and plan for its increased integration into the power grid.

3. Real Estate Features: Real estate firms could utilize the model to identify properties equipped with solar panels, adding it as a selling point in their listings or aiding in selecting properties for green investor portfolios.

4. Monitoring Energy Transformation: Organizations focusing on climate change could use the model to track the progress of the transformation to renewable energies, providing real-world data to support their efforts.

5. Disaster Recovery: In post-disaster scenarios, the model could be used to assess the state of solar infrastructure, helping prioritize recovery efforts and maximizing the restoration of self-sufficient energy sources.

This Dataset contains field research raw data, analysis spreadsheet, photos, and final report from the Hathaway Solar Patriot House Building America Case Study project. This dataset details the monitored and modeled performance of a solar home outside of Washington, D.C. We modeled the home energy performance using DOE 2.2, performed numerous short-terms tests on the home, and monitored its occupied performance for 29 months. The home used modular construction, solar water heating, a ground-coupled heat pump, efficient appliances, and compact fluorescent lighting to reduce its energy consumption by 35% compared to the Building America research benchmark home. The addition of 6kW of photovoltaics (PV) increased the savings to 67% compared to the Building America research benchmark. A more efficient shell to reduce space conditioning loads would have brought the home closer to its zero energy goals. However, even with efficient lighting and appliances, the lights, appliance and plug loads were a significant energy consumer. About 4 kW of PV was required to meet the needs of these loads alone. To achieve the zero energy goal with no further efficiency increases, the Hathaway house would need about 2.6 kW of PV in addition to the 6.0 kW it now has. Applying advanced efficiency measures available or being developed, such as heat-recovery ventilation, superinsulation, and electrochromic windows, could reduce the heating, cooling, and domestic hot water (DHW) energy use to less than 1700 kWh per year, an 88% reduction in these loads from the Building America Benchmark. At this efficiency level, the appliance and plug loads come to dominate energy consumption and account for nearly 70% of the total energy use. This analysis points out that even with highly effective energy-savings technologies (pushed beyond levels currently practical), whole-house energy use reduction by efficiency measures is only about 60% without also reducing the energy use of appliances and plug loads largely considered outside the designers jurisdiction.

This dataset depicts the Kittias County Solar Power Production Facilites (SPPFs) Overlay, pursuant to Kittitas County Code 17.61C. This dataset was derived in part from the Washington Department of Agriculture (WSDA) Agricultural Land Use dataset. The dataset was used to identify currently cultivated lands within Kittias County with conservation value as agricultural resources. These lands were desingated as Zone 1, where SPPFs are prohibited, and other lands within th County were designated as Zone 2, where SPPfs are allowed with Conditional Use Permit approval. Fields:Zone: The Overlay Zone depicted. Description: Summary of the regulatory status of the lands depicted on the map.

This table contains figures on the calculated power of solar panels in kW and the number of installations for companies and private homes for the years 2012-2019. These figures can be broken down by municipality, province and country and from 2018 also by subRES region (RES stands for Regional Energy Strategy). The regional breakdown for all years is based on the municipal division of 1 January 2019.

Data available from: 2012

Status of the figures: The data until 2019 is final.

Changes as of 9 July 2021: In the table a number of cells were unjustly suppressed, these are now being released. A number of double registrations of installations have also been discovered and restored.

Changes as of June 2021: The figures for 2019 have been revised and final. Between the publication of the provisional figures 2019** and the final figures 2019 there has been a change in the source files. By mid-2020, the Network Managers replaced the Production Installation Register (PIR) with the Central Registration of System Elements. (CERES). PIR and CERES are the main sources of information about smaller solar power plants. CERES could be used for the first time for the final figures for 2019. Installations up to 2018 are based on data from the PIR. For installations starting in the 2019 installation year, the data comes from CERES. In all cases, if an installation occurs in both PIR/CERES and CertiQ data, the CertiQ data will be used. CertiQ certifies energy generated from the renewable sources and contains mainly, but not exclusively, larger installations.

Changes as of 10 December 2020: A new region layout has been added. These are the sub-regional energy strategy regions (sub RES regions). Due to changes in the classification, figures from previous years have also changed. The figures for 2019 have been revised and are now further preliminary figures.

When are new figures coming? This table will be closed in November 2021. In June 2021, the district and neighbourhood data for the final figures 2019 were published. In July 2021, the provisional figures on the prepared capacity became available in a new table in 2020. In this new table, the municipal classification of that year will be kept for each reporting year.

Green building increases the efficiency with which buildings use resources – energy, water, and materials – while reducing building impacts on human health and the environment. Adding renewable energy systems can reduce operating costs and the greenhouse gas emissions associated with buildings.For more information, visit the links below: Sustainable Buildings in the City Adding Solar Power: CitySolar Please Note: Solar data may be incomplete due to limited tracking tools prior to June 2014. Email Usabout a system not currently shown in the map.

This dataset contains unmanned aerial vehicle (UAV) imagery (a.k.a. drone imagery) and annotations of solar panel locations captured from controlled flights at various altitudes and speeds across two sites at Duke Forest (Couch field and Blackwood field). In total there are 423 stationary images and corresponding annotations of solar panels within sight, along with 60 videos taken from flying the UAV roughly at either 8 m/s or 14 m/s. In total there are 2,019 solar panel instances annotated.Associated publication:“Utilizing geospatial data for assessing energy security: Mapping small solar home systems using unmanned aerial vehicles and deep learning” [https://arxiv.org/abs/2201.05548]Data processing:Please refer to this Github repository for further details on data management and preprocessing: https://github.com/BensonRen/Drone_based_solar_PV_detection. The two scripts included enable the user to reproduce the experiments in the paper above.Contents:After unzipping the package, there will be 3 directories:1. Train_val_set: Stationary UAV images (.JPG) taken at various altitudes in the Couch field of Duke Forest for training and validation purposes, along with their solar PV annotations (.png)2. Test_set: Stationary UAV images (.JPG) taken at various altitudes in the Blackwood field of Duke Forest for test purposes, along with their solar PV annotations (.png)3. Moving_labeled: Images (img/*.png) capture from videos moving with two speed modes (Sport: 14m/s, Norma: 8m/s) at various altitudes and their solar PV annotations (labels/*.png)For additional details of this dataset, please refer to REAMDE.docx enclosed.Acknowledgments: This dataset was created at the Duke University Energy Initiative in collaboration with the Energy Access Project at Duke and RTI International. We thank the Duke University Energy Data Analytics Ph.D. Student Fellowship Program for their support. We also thank Duke Forest for use of the flight zones for data collection.