Prediction of solar panel voltage based on weather conditions using machine learning algorithm - Deya Al-Deen Nayif Bakheet
Environmental issues are among the critical problems plaguing the world. Since the Solar energy has been considered as one of the fastest growing among the renewable energy sources that addresses the energy demand in producing clean energy. The continuous development in solar-based harvesting technology contributes to increase reliability of the solar power technology. One of the key issues for solar energy is the dependency to the weather conditions, which generates energy fluctuations in the amount of energy generated, and furthermore leads to electric grids to be unstable. Therefore, there is a need to have an accurate prediction of solar energy generation. The main objective is to predict the amount of energy generated by solar panels in different weather conditions using supervised machine learning. The model of prediction is developed based on 2000 dataset collected using sensors through Internet of Things platform. The solar energy dataset collected represents parameters of weather conditions, namely: outlook; temperature· and humidity. The model of prediction algorithms such as Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), and Random Forest (RF) are selected because these three algorithms are supervised machine learning techniques. The three algorithms were analysed and compared using correlation coefficient (r) and error ratio mean absolute error (MAE), to measure their performance. The use of r evaluates the ratio of accuracy and strength of the relationship between weather condition parameters and solar energy produced. The r used to measure how strong a relationship is between two variables, on a scale that varies from + 1 through 0 to - 1, the higher r-value represents a higher accuracy of the prediction. MAE measures the average magnitude of the errors in the test sample of the absolute differences between prediction and actual value, in a set of predictions. Where lower MAE value indicates that the algorithm has higher accuracy and less error rate. The result shows that RF has the best r of voltage compared to KNN and ANN algorithms with 97.26%, 97.04%, and 95.11 % respectively. RF has the lowest error rate compared to KNN and ANN algorithms with 11.54%, 11.60% and 13.61% respectively. Higher r and lower MAE represent higher prediction accuracy. The study was able to create accurate prediction model using supervised machine learning techniques for the solar panel voltage output. Using the prediction model, electric grid operators can predict accurately the voltage that the solar power plant will produce based on weather conditions.