A new dual-beam technique for precise measurements of spectral reflectance in the field

Field spectral measurements made using the single - beam method often include errors due to variation in illumination between measurement of the target and the reference (panel or cosine -corrected receptor). Although the dual-beam method avoids these errors, it introduces greater complexity due to the need to intercalibrate the two sensor heads used, and it is significantly more expensive. This paper describes an alternative dual-beam method which uses a neural network to estimate the complete irradiance spectrum from measurements made in 7 narrow bands. These narrow band measurements of irradiance may be made with a simple filter-based radiometer, thus avoiding the expense and complexity of a second spectroradiometer. The new technique has been tested using irradiance spectra from both continental and maritime locations

Multi-time-horizon Solar Forecasting Using Recurrent Neural Network

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE 2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i

Estimating spectral irradiance from measurements in seven spectral bands

Accurate measurement and characterisation of fluctuations in the irradiance environment is important for many areas of optical remote sensing. This paper reports a method of estimating spectral irradiance over the VNIR region (400 - 1100nm) from the radiance of a calibrated reference panel, measured in seven narrow (10nm) spectral bands. Earlier work established the potential for estimating spectral irradiance from multi-band data using a neural network technique (Milton et al., 2000). The approach described here uses linear regression analysis to regenerate the irradiance spectrum from data in seven reference wavelengths. The method was tested using data from a specially designed multiband radiometer – the INdependent SPectral IRradiance Estimator (INSPIRE). The irradiance spectrum was partitioned into a number of distinct regions within each of which the spectral irradiance was estimated from irradiance measured at one of the reference wavelengths. The precision of the method was found to be better than ±5% over most wavelengths from 400nm to 1100nm. Furthermore, the slope coefficients of the individual regression models were found to be sensitive to the sky radiance conditions, especially over the region 600-760nm, and improvement in the precision of the predicted spectrum (to within ±3%) was obtained by taking the diffuse-to-global (D:G) irradiance ratio at the time of measurement into account

A compact energy harvesting system for outdoor wireless sensor nodes based on a low-cost in situ photovoltaic panel characterization-modelling unit

This paper presents a low-cost high-efficiency solar energy harvesting system to power outdoor wireless sensor nodes. It is based on a Voltage Open Circuit (VOC) algorithm that estimates the open-circuit voltage by means of a multilayer perceptron neural network model trained using local experimental characterization data, which are acquired through a novel low cost characterization system incorporated into the deployed node. Both units—characterization and modelling—are controlled by the same low-cost microcontroller, providing a complete solution which can be understood as a virtual pilot cell, with identical characteristics to those of the specific small solar cell installed on the sensor node, that besides allows an easy adaptation to changes in the actual environmental conditions, panel aging, etc. Experimental comparison to a classical pilot panel based VOC algorithm show better efficiency under the same tested conditions

Real-time fault detection in photovoltaic power plants

Climatic changes are one of the biggest problems that humanity faces and renewable energies are a big weapon to fight this threat. Solar energy is one of the renewable energy sources in current use and to produce this type of energy there are several solar plants placed across the country. These giant plants are made of many sets of solar panels (called arrays) which are responsible for converting solar energy into electricity. One of the critical aspects of these plants' operation is the early detection of solar panel malfunctions. The current methods in use are expensive and consume a lot of time, meaning that, in some cases, the faults are detected only a year later, causing a huge financial impact on the companies responsible for the plants' operation. To cut these losses and to detect the faults as early as possible, this dissertation presents a real-time system capable of detecting malfunctions in a solar panel array. The node should be placed in the array's junction box and detects if an array has a faulty panel. The faults are detected comparing the array's output (voltage and current) with the output of an artificial neural network that models the array's behaviour using the real-time solar irradiance and temperature values. The neural network uses the measured values to carry out an online learning process, improving the network performance. Due to the plant's extension, a low power wide area network (LORAWAN), is used to send the array status and the data collected to the cloud, where they are processed and presented in a dashboard