Solar power generation prediction based on deep Learning

Recently, the fraction of the grid energy generated by renewables is significantly increased by smart grid initiatives. In General, power generation is irregular and uncontrollable while incorporating renewables into the Grid has been considered a significant challenge. Therefore, it is necessary to forecast renewables' future production because Grid will deliver generators to meet demand differently. Although sophisticated prediction models for large-scale solar farms can be built manually, designing them for distributed production in millions of homes across the Grid is difficult. The above problems are solved by Traditional Encoder Single Deep Learning (TESDL) method introduced for weather forecasts using deep Learning Techniques. Several regression techniques are compared for generating prediction models, including low linear squares and help vector machines (VM) using Multiple Short-Term Functions (MSTF). Developers test the model's accuracy using historic TESDL forecasts and solar-intensity readings from nearly a year's use of a weather station. Our findings show that 27% improvement in accuracy factor in VM-based forecast models shows improved performance than conventional methods.

Automated summary

In recent years, the proportion of grid energy generated by renewables has significantly increased due to smart grid initiatives. Forecasting future renewable energy production and developing prediction models for distributed production across millions of homes on the grid are important challenges that have been addressed by the Traditional Encoder Single Deep Learning (TESDL) method. By comparing various regression techniques, it was found that the accuracy factor in VM-based forecast models showed a 27% improvement over conventional methods.