# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 3 State of the Art and Related Products
### Peer Review Literature
#### 3.1.4 Site Selection for Renewable Energy Platforms via Satellite Imagery  
One of the goals of sustainable development is affordable and clean energy (SGD7). Europe has consistently increased its renewable energy production and, in 2020, it represented 22.1 % of the energy consumed in the whole continent, which is almost 2 percentage points above the target of 20 %. Greece has seen an unprecedented increase in renewable energy consumption in the past decade, with renewable energy source shares being nearly doubled. According to the World Bank and Eurostat data, this increase went from 6.9% in 2004 to 15.5 % in 2017, and 21.7% of gross final energy consumption exceeded the set goal. The Greek energy sector, however, still largely depends on imported fossil fuels. As of 2017, almost half of its energy needs were covered by petroleum products, used in the transport sector but also converted into electricity. Greece's electrical independence relies heavily on lignite, making it one of the only nine EU member states that still produce it endogenously and place it among the top three countries that still use it for electricity consumption and heat ([[https://ec.europa.eu/eurostat/statistics-explained/index.php?](https://ec.europa.eu/eurostat/statistics-explained/index.php?)]([https://ec.europa.eu/eurostat/statistics-explained/index.php?](https://ec.europa.eu/eurostat/statistics-explained/index.php?)) title=Production_of_lignite_in_the_EU_statistics, accessed on 1 September 2023). However, there have been concerns with the surge in solar plant installation, including possible instability in ecologically fragile regions (i.e., biodiversity loss, local climatic change, and food sovereignty). In order to better monitor and prevent these problems, geo-referenced data are a necessity, which are unfortunately lacking. High-quality data to build accurate models for predicting the behavior of solar radiation are required for optimal solar energy systems management. The impact of a PV plants' installation can be monitored efficiently with the use of spatial distribution and dynamic data generated through remote sensing. The need for Greece to abide by the EU standards and the decreasing consumption of fossil fuels, the fact that the energy sector has a higher contribution to gross value added than most European countries, and the generation potential of electricity due to its climate along with government support make Greece a great place for investment and economic growth. A lot of public and private organizations have invested in solar and wind renewable energy ([[https://www.enterprisegreece.gov.gr/en/invest-in-greece/sectors-for-growth/energy](https://www.enterprisegreece.gov.gr/en/invest-in-greece/sectors-for-growth/energy)]([https://www.enterprisegreece.gov.gr/en/invest-in-greece/sectors-for-growth/energy](https://www.enterprisegreece.gov.gr/en/invest-in-greece/sectors-for-growth/energy)), accessed on 1 September 2023). Next we present some applications of data-driven remote sensing for photovoltaic and wind turbine site selection as well as monitoring environmental change detection due to the extensive use of lignite. Ref. [47] proposes an ML methodology for site selection and solar radiation forecasting. Specifically, they combined solar radiation ground-truth data and satellite solar radiation data from geostationary meteorological satellites to obtain long-term solar information with improved spatiotemporal information (site adaptation). Then, they constructed an LSTM Deep Learning model that takes these improved data as input and makes accurate predictions regarding solar radiation over a particular region, offering an almost 40% performance increase over traditional statistical methods. Ref. [48] uses freely accessible Landsat 8 OLI images to identify and map the spatial distribution of photovoltaic (PV) plants on a local and global scale. They combined spectral bands and indices for PV extraction, such as NDVI, BI, and BUAL, from the satellite imagery and fed them to high-performance machine learning models.