Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.

Forecasting plays an indispensable role in grid integration of solar energy, which is an important pathway toward the grand goal of achieving planetary carbon neutrality. This rather specialized field of solar forecasting constitutes both irradiance and photovoltaic power forecasting. Its dependence on atmospheric sciences and implications for power system operations and planning make the multi-disciplinary nature of solar forecasting immediately obvious. Advances in solar forecasting represent a quiet revolution, as the landscape of solar forecasting research and practice has dramatically advanced as compared to just a decade ago. Solar Irradiance and Photovoltaic Power Forecasting provides the reader with a holistic view of all major aspects of solar forecasting: the philosophy, statistical preliminaries, data and software, base forecasting methods, post-processing techniques, forecast verification tools, irradiance-to-power conversion sequences, and the hierarchical and firm forecasting framework. The book’s scope and subject matter are designed to help anyone entering the field or wishing to stay current in understanding solar forecasting theory and applications. The text provides concrete and honest advice, methodological details and algorithms, and broader perspectives for solar forecasting. Both authors are internationally recognized experts in the field, with notable accomplishments in both academia and industry. Each author has many years of experience serving as editors of top journals in solar energy meteorology. The authors, as forecasters, are concerned not merely with delivering the technical specifics through this book, but more so with the hopes of steering future solar forecasting research in a direction that can truly expand the boundary of forecasting science.

This book contains the proceedings of the 4TH International Conference on Computational Methods in Science and Technology (ICCMST 2024). The proceedings explores research and innovation in the field of Internet of things, Cloud Computing, Machine Learning, Networks, System Design and Methodologies, Big Data Analytics and Applications, ICT for Sustainable Environment, Artificial Intelligence and it provides real time assistance and security for advanced stage learners, researchers and academicians has been presented. This will be a valuable read to researchers, academicians, undergraduate students, postgraduate students, and professionals within the fields of Computer Science, Sustainability and Artificial Intelligence.

Solar photovoltaic (PV) capacity is rapidly growing across the world. However, the volatility of cloud movement introduces significant uncertainty in short-term solar PV output, which can complicate the operation of modern power systems. Cloudy days remain challenging for modern short-term solar forecasting algorithm. An improved short-term forecast benefits all participants of the sub-hourly power market. This work proposes a specialized convolutional neural network (CNN) "SUNSET" for short-term solar PV output forecasting. Its suitability is first tested on now-casting, i.e. inferring contemporaneous PV output from sky images. On a system with a rated capacity of 30.1 kW, the baseline SUNSET model achieved an RMSE of 1.01 kW on the sunny test set, 3.30 kW on the cloudy test set, and 2.40 kW overall. This validates the sky images' close correlation with PV panel outputs and that a CNN is suitable to extract this correlation. Extensive experiments are done to optimize the structure of SUNSET. In terms of depth, having three convolutional layers and one fully-connected layer produces the best result. Both types of neural nets are found to be crucial for model performance. In terms of width, 48 filters in the convolutional layers and 2048 neurons in the fully-connected layers provide the best performance. In terms of image resolution, 64 x 64 is the optimal point, as either finer or coarser resolution results in worse RMSE. Two further techniques are also found to be useful: drop-out increases the robustness for generalization while ensemble modeling decreases forecast error. For forecast, the SUNSET model is augmented in two key aspects, the usage of hybrid input and temporal history. PV output history is injected mid-way in the model to be joined with the processed image features. The temporal history of sky images are included by concatenating the images in the color channel. On a 1-year database, the "baseline'' model achieves a 15.7% forecast skill in all weather conditions, and a 16.3% forecast skill in the more demanding cloudy conditions, relative to a smart persistence forecast. Optimal input and output configurations for forecast are also explored. In terms of input, both sky images and PV output history are found to be crucial. Output-wise, training against PV output significantly out-performs training against clear sky indices (CSI). Careful down-sampling can reduce the training time by as much as 83% without affecting accuracy. For lag term configurations, using the same length of history as the forecast horizon is a good heuristic, while using slightly shorter history yields a modest 0.5% - 0.9% improvement. Last but not least, a two-stage optimization framework is proposed to quantify the value of short-term solar forecast. Design optimization in the first stage solves for resource capacity, while the receding horizon control (RHC) in the second stage simulates a power system's operation for a year. Within this framework, we consider a microgrid scenario which have battery as an option, and a demand charge scenario which allows grid import. In the settling process of the RHC stage, batteries can be utilized and redesigned to address forecast error in the microgrid scenario, while grid import is incurred in the demand charge scenario for the same purpose. For the microgrid scenario, a perfect forecast can reduce overall cost by 3.6% to 12.5% comparing to a persistence forecast. The largest cost savings are achieved with medium solar penetration of 30% to 60%. For the demand charge scenario, a perfect forecast can reduce total cost by 8.9% to 25.6%. For SUNSET with a forecast skill of 15.7%, we can expect a cost saving of 1-2% or 2-5% respectively in these two scenarios.

This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies

A very short term solar power forecasting technology which uses ground-based visible wavelength imagery is presented. A sky camera system suitable for use as a solar power forecasting tool is described. Relevant imaging considerations are discussed, including the need for high dynamic range imaging of the daytime sky and an associated stray light assessment. To photogrammetrically calibrate this sky camera system, a general camera model applicable to a fixed focal length photo objective lens with significant radially symmetric distortion is developed, and an accurate calibration technique for a stationary, skyward pointing daytime camera using the sun's position is given. Remote sensing algorithms used in the solar forecasting process are detailed, including clear sky characterization, cloud detection, cloud velocity estimation, and cloud height estimation using stereography. A cloud stereo photogrammetry method which provides dense 3D cloud position is presented. Correspondence is automatically determined using intra-scanline dynamic programming applied to a normalized cross correlation matching metric; an ordering constraint is implicit in the approach used. Using the described remote sensing tools and methods, a complete solar power forecasting framework is detailed. The method is based on the estimation of cloud shadow position via ray tracing, and the forecast position of the cloud shadows relative to solar collectors. A ray tracing procedure that works with a planar mapping of cloud position is used to compute shadow position. Cloud transmissivity is characterized using past observations and applied to forecast cloud positions. The application of the procedure to two case studies: the UCSD DEMROES weather station network, and a 48MW solar photovoltaic power plant is presented. A comparison of the forecasting performance using a common Total Sky Imager is compared to the UCSD Sky Imager, where it is shown that the UCSD Sky Imager performs better overall.

Solar forecasts are important for low-cost integration of solar energy into the smart grid. Accurate intra-hour predictions of irradiance quantify the variability of solar power at ground level, reduce the uncertainty in power output from solar farm, and are important for real-time grid balancing and management. A multilayered-hybrid-algorithm method is developed to generate real-time intra-hour prediction intervals (PIs) for both global and direct solar irradiance. This forecasting method integrates stochastic learning methods for the prediction of solar irradiation and local sensing techniques for the introduction of exogenous inputs. The research of the proposed forecasting method consists of four objectives : (1) Development of a smart forecasting engine based on advanced stochastic learning methods. (2) Development of an image-based cloud detection system using a cost-competitive fish-eye camera. (3) Integration of the smart forecasting engine with the cloud detection system to create a high-fidelity forecasting model. (4) Development of a hybrid algorithm to provide prediction intervals for the integrated forecasting model. The forecasting method introduced here is deployed in real-time and achieves forecast skills up to 20% over the reference persistence model. Real-time PIs generated from this method achieve coverage probabilities which are consistently higher than the nominal confidence level (90%) regardless of weather condition.