IoT and machine learning models for multivariate very short-term time series solar power forecasting

In solar energy generation, the inherent variability caused by cloud cover and weather events often leads to sudden fluctuations in power outputs. Addressing this challenge, the authors' study focuses on very short-term solar irradiance (SI) prediction. Leveraging multivariate time series datasets, the authors improve very short-term SI predictions. To achieve accurate very short-term SI predictions, the authors employ machine learning techniques throughout the forecasting process. Additionally, the authors' work pioneers the integration of the Internet of Things (IoT) into solar power systems, a novel approach in the field. The authors leverage LoRa (long range) technology for low-cost, low-power, and long-range wireless control networks. The authors' study focuses on SI forecasting using long short-term memory and bi-directional long short-term memory (Bi-LSTM) models, achieving high accuracy. The SI forecasts are evaluated in terms of root-mean-square error (RMSE) and mean absolute error in relation to meteorological data and sky image data. The improvement in performance can be attributed to the Bi-LSTM's bidirectional nature, allowing it to incorporate future information during training, thereby enhancing its predictive capabilities. Overall, the results suggest that the Bi-LSTM model is more suitable for accurately forecasting SI, particularly in scenarios requiring short-term predictions based on rapidly changing environmental factors. The authors' work pioneers the integration of the Internet of Things (IoT) into solar power systems. The authors' study focuses on solar irradiance forecasting using long short-term memory and Bi-LSTM models, achieving high accuracy. The solar irradiance forecasts are evaluated in terms of root-mean-square error (RMSE) and mean absolute error (MAE) in relation to meteorological data and sky image data. image