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

Uncertainty estimation of visual attention models using spatiotemporal analysis

In this dissertation, we analyze eye tracking data and video content to discover general patterns of human visual attention that can be used for estimating the reliability or the confidence of video saliency maps that are often used in many video processing applications. We, first, analyze eye-fixation data and discover patterns such as map consistency and scene motion to be used for uncertainty estimation. Based on such analysis, we introduce a procedure to estimate the correlation between eye-fixation data of a given video by using its corresponding optical flow map. We, also, utilize the eye-fixation correlation analysis to design an unsupervised video feature for uncertainty estimation based on local spatiotemporal neighborhoods. We combine our findings from eye-fixation correlation study and the analysis of the unsupervised uncertainty estimation feature for video saliency with a data-driven approach to directly obtain a multi-factor estimation model that is both computationally-efficient and effective in estimating uncertainty in the application of video saliency detection.Ph.D

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

FPGA-based real-time implementation for direction-of-arrival estimation

Direction-of-arrival (DOA) estimation of radio signals is of utmost importance in many commercial and military applications. In this study, the authors propose an efficient field-programmable gate array (FPGA) architecture for implementing a recently published DOA estimation algorithm. This algorithm estimates DOAs by making use of QR decomposition of the received data matrix of four- and eight-element uniform linear antenna arrays. The hardware implementation has been thoroughly analysed and experimentally validated by building a real-time prototype of the DOA estimation algorithm. The experimental results show good agreement between DOA estimates obtained by the prototype and true values

Machine Learning-Based Optical Performance Monitoring for Super-Channel Optical Networks

In this paper, and for the first time in literature, optical performance monitoring (OPM) of super-channel optical networks is considered. In particular, we propose a novel machine learning OPM technique based on the use of transformed in-phase quadrature histogram (IQH) features and support vector regressor (SVR) to estimate different optical parameters such as optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD). Two transformation methods, the two-dimensional (2D) discrete Fourier transform (DFT) and 2D discrete cosine transform (DCT), are applied to the IQH to extract features with a considerably reduced dimensionality. For the purpose of simulation, the OPM of a 7 × 20 Gbaud dual-polarization–quadrature phase shift keying (DP-QPSK) is considered. Simulations reveal that it can accurately estimate the various optical parameters (i.e., OSNR and CD) with a coefficient of determination value greater than 0.98. In addition, the effectiveness of proposed OPM scheme is examined under different values of polarization mode dispersion and frequency offset, as well as the utilization of different higher order modulation formats. Moreover, proof-of-concept experiments are performed for validation. The results show an excellent matching between the simulation and experimental findings