Solar Generation Prediction using Artificial Intelligence: A Review

Solar energy generation is one of the most promising and fastest-growing renewable energy sources for the generation of useful energy worldwide. Forecasting of solar power is the most essential for the planning of grid operations, mainly in residential microgrids, to optimize and manage the energy produced in a dispatchable trend. Due to the inability of deterministic methods to accurately forecast solar power generation due to their dependency on natural inputs, Artificial Intelligence (AI) based techniques are required to be implemented.  AI techniques clubbed with stochastic methods are considered to be highly effective for solar generation forecasting. In this review, various artificial intelligence-based supervised and unsupervised learning methods for solar energy generation prediction are analyzed. The use of weather and environmental inputs for supervised learning is also compared. The accuracy of prediction of solar generation using several AI, Machine Learning, and Neural Network-based techniques are also analyzed in the paper. The paper presents an overall picture of the use of Artificial-Intelligence based techniques in solar generation prediction in the world

A Study of Machine Learning Techniques for Daily Solar Energy Forecasting using Numerical Weather Models

Proceedings of: 8th International Symposium on Intelligent Distributed Computing (IDC'2014). Madrid, September 3-5, 2014Forecasting solar energy is becoming an important issue in the context of renewable energy sources and Machine Learning Algorithms play an important rule in this field. The prediction of solar energy can be addressed as a time series prediction problem using historical data. Also, solar energy forecasting can be derived from numerical weather prediction models (NWP). Our interest is focused on the latter approach.We focus on the problem of predicting solar energy from NWP computed from GEFS, the Global Ensemble Forecast System, which predicts meteorological variables for points in a grid. In this context, it can be useful to know how prediction accuracy improves depending on the number of grid nodes used as input for the machine learning techniques. However, using the variables from a large number of grid nodes can result in many attributes which might degrade the generalization performance of the learning algorithms. In this paper both issues are studied using data supplied by Kaggle for the State of Oklahoma comparing Support Vector Machines and Gradient Boosted Regression. Also, three different feature selection methods have been tested: Linear Correlation, the ReliefF algorithm and, a new method based on local information analysis.Publicad

Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning

Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. The~proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The~performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The~datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time.Comment: Published in MDPI Electronics Journa

Short Term Forecasting of Solar Radiation

This paper details how to predict solar radiation at a location for the next few hours using machine learning techniques like Facebook’s Prophet, and Amazon’s DeepAR+. Multiple techniques like AutoRegressive (ARIMA) and Exponential Smoothing (ES) have been used to forecast solar radiation, but they lack accuracy and are not scalable. Whereas Prophet, and Amazon’s DeepAR+ are scalable, accurate, and easily integrated into other machine learning techniques. This will be the first time where the combination of these techniques along with Linear Regression, Random Forest, XGBoost and Decision Tree will be leveraged to forecast solar radiation for the short term. Predicting solar energy accurately depends on multiple factors (including weather conditions) that make forecasting highly resource-intensive, and accuracy remains a challenge. Improving the accuracy of the short-term forecast of solar energy production would provide a massive value to the companies operating IoT Devices and drones to have a more efficient operation and reduced cost. The objective is to improve the accuracy of forecasting short-term solar radiation to power drones and IoT devices, leveraging the ensemble techniques by combing the outcome of Prophet and DeepAR+. Facebook’s Prophet, and Amazon’s DeepAR+ used to carry out shortterm solar forecasting can be scaled by leveraging the supercomputer. Amazon’s DeepAR+ runs on the AWS cloud platform, so they align well with scaling and bring in all the enhancement that comes with cloud technology. Multiple models were used to identify the best way to forecast short-term solar radiation. Random Forest and ensemble models outperformed the Facebook Prophet and Amazon’s DeepAR+, achieving a coefficient of determination R2 of 99 % in Dallas, Texas. Ensemble Model was created to minimize the bias and variance of the outcome

Forecasting hourly electricity generation by a solar power plant using machine learning algorithms

Relevance. The need to develop energy-saving approaches through the use of data mining tools to improve the efficiency of management decision-making and more optimal use of energy resources. Forecasting the amount of electric energy generated by a solar power plant will allow optimal electricity distribution in decentralized systems. Information about the amount of electricity generated by a solar power plant and transmitted to a grid at every hour will allow planning the use of generated electricity and its distribution more reasonably. Also, the presence of a reliable forecast will allow embedding a predictive model into the micro-grid management subsystem. This will facilitate the integration of centralized electrical networks and distributed generation facilities. Aim. To analyze scientific papers containing proposals to improve the accuracy of determining the amount of electricity generated by solar power plants; to create machine learning models that allow you to create a short-term forecast of electricity generation by a solar power plant. Objects. Solar power plant named after A.A. Vlaznev (Sakmarskaya SPP), Orenburg region, Orsk, Oktyabrsky district, geographical coordinates: 51.266023, 58.474689. Methods. Analytical method, methods of mathematical statistics, methods of machine learning, complex generalization of scientific achievements and practical experience in the use of data processing tools in the tasks of forecasting electricity generation by solar power plants. Results. The authors have carried out the review of literature sources reflecting the results of using modern data mining tools in predicting the magnitude of electricity generation by solar power plants. The paper considers various approaches to forecasting electricity generation at solar power plants. The analysis of factors used in forecasting is carried out. The authors obtained the results of theoretical and applied nature. The results  consist in recommendations on using exogenous variables in predicting power generation at SPP, as well as some machine learning algorithms in construction of predictive models. These recommendations were obtained in generalizing the results of the applied research

An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning

For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable operation of the electric grid rises due to the variability of solar energy. The higher the uncertainty in the generation, the greater the operating-reserve requirements, which translates to an increased cost of operation. In this research work, we propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance forecasting using recurrent neural networks (RNN) and long-short-term memory networks (LSTMs). This paper also lays out a framework for extending this modeling approach to intra-hour forecasting horizons thus, making it a multi-time-horizon forecasting approach, capable of predicting intra-hour as well as intra-day solar irradiance. We develop an end-to-end pipeline to effectuate the proposed architecture. The performance of the prediction model is tested and validated by the methodical implementation. The robustness of the approach is demonstrated with case studies conducted for geographically scattered sites across the United States. The predictions demonstrate that our proposed unified architecture-based approach is effective for multi-time-scale solar forecasts and achieves a lower root-mean-square prediction error when benchmarked against the best-performing methods documented in the literature that use separate models for each time-scale during the day. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites compared to the ML-based best-performing method reported in the literature. Additionally, the proposed method enables multi-time-horizon forecasts with real-time inputs, which have a significant potential for practical industry applications in the evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio