Energy

The energy industry powers modern life and fuels economic expansion. Every other industry depends on the energy sector to function. However, the energy industry is facing meaningful changes, including the push to reduce carbon emissions, the need for greater efficiency, and the rise of disruptive technological advancements.

EO data plays a vital role in assessing renewable energy systems' feasibility and prospecting for resources. Solar energy resource assessment, photovoltaics (PV) installations, wind power estimation, wind farm siting, and environmental impact assessment are long-established uses of EO data. Similarly, EO-derived thermal infrared data, surface wavelength measurements, and gravity anomalies have been used for more than a decade in large land areas' geothermal prospecting. EO data are also commonly used to assess water quantity and availability for hydroelectric project planning and monitoring, determination of ideal dam locations and reservoir size, and environmental impact from rerouting or damming water.

Solar energy

Solar power could meet the entire energy needs of homes; the rooftops are suitable for solar power production; solar energy resources can be optimized by placing photovoltaic panels on rooftops around the country. The geography of the area: sun angles, wind patterns, street widths, and building density and height. The orientation of buildings on a diagonal grid to provide maximum natural shading. Cloudeo helps its customers in the below areas.

• Identify the rooftop and select the area that is potential for solar installation
• Calculate the solar potential area
• Calculate the solar radiation of the region
• Calculate the amount of energy generated from the solar panel
• Find out the power generation from each rooftop

Wind energy

Earth Observation data shows where the most wind energy can be harnessed while minimizing the environment's cost and impact. Provide accurate and timely information on various environmental parameters such as wind and waves, weather conditions, topography, and vegetation cover. They determine the most favorable wind farms' locations based on the cost of transmission, locations of load centers and wind resources, and the electrical grid layout. This information is essential for selecting a suitable wind farm site and ensuring optimal exploitation from its operation.

Spatial based modeling enabled terrain analysis, which significantly impacts the wind's quality at a particular site. Space-based assessments are beneficial for offshore sites as measurements are challenging to obtain from other means. Three different types of sensors are mostly used in the life cycle of a wind farm (on land and offshore): radar altimeters measuring parameters related to the state of the sea such as significant wave height and wind speed, scatterometers measuring wind fields, and image radar sensors measuring wind, wave, terrain roughness, and orography. These data's unique characteristics complement traditional ground-based measurements and make them indispensable for planners, investors, and decision-makers.

Grid Management

The success of grid management, i.e., grid infrastructure operation and reliability, is often dependent on environmental factors. It is estimated that about 90 percent of load forecasting errors are weather-related. The matching of supply and demand necessitates accurate data, as inaccuracies can instigate brownouts that impact other sectors. Energy forecasting models and infrastructure planning to improve grid reliability using EO-derived data on current weather conditions and historical climate trends. Outage magnitude can be assessed and monitored with nighttime lights EO data. Inadequate line maintenance is also a cause of outages. Overhead transmission lines require routine vegetation management to mitigate the potential for falling limbs. Many global EO-derived products that measure vegetation are freely accessible, such as Terra/Aqua MODIS Normalized Difference Vegetation Indices (NDVIs) available at variable temporal and spatial resolutions.