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Register a new userPredicting Solar Irradiance in Singapore
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Solar irradiance is the primary input for all solar energy generation systems. The amount of available solar radiation over time under the local weather conditions helps to decide the optimal location, technology and size of a solar energy project. We study the behaviour of incident solar irradiance on the earths surface using weather sensors. In this paper, we propose a time-series based technique to forecast the solar irradiance values for shorter lead times of upto 15 minutes. Our experiments are conducted in the tropical region viz. Singapore, which receives a large amount of solar irradiance throughout the year. We benchmark our method with two common forecasting techniques, namely persistence model and average model, and we obtain good prediction performance. We report a root mean square of 147 W/m^2 for a lead time of 15 minutes.
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We analyse the time series of solar irradiance measurements using chaos theory. The False Nearest Neighbour method (FNN), one of the most common methods of chaotic analysis is used for the analysis. One year data from the weather station located at Nanyang Technological University (NTU) Singapore with a temporal resolution of $1$ minute is employed for the study. The data is sampled at $60$ minutes interval and $30$ minutes interval for the analysis using the FNN method. Our experiments revealed that the optimum dimension required for solar irradiance is $4$ for both samplings. This indicates that a minimum of $4$ dimensions is required for embedding the data for the best representation of input. This study on obtaining the embedding dimension of solar irradiance measurement will greatly assist in fixing the number of previous data required for solar irradiance forecasting.
Context. There is no consensus on the amplitude of the historical solar forcing. The estimated magnitude of the total solar irradiance difference between Maunder minimum and present time ranges from 0.1 to 6 W/m2 making uncertain the simulation of the past and future climate. One reason for this disagreement is the applied evolution of the quiet Sun brightness in the solar irradiance reconstruction models. This work addresses the role of the quiet Sun model choice and updated solar magnetic activity proxies on the solar forcing reconstruction. Aims. We aim to establish a plausible range of the solar irradiance variability on decadal to millennial time scales. Methods. The spectral solar irradiance (SSI) is calculated as a weighted sum of the contributions from sunspot umbra/penumbra, fac- ulae and quiet Sun, which are pre-calculated with the spectral synthesis code NESSY. We introduce activity belts of the contributions from sunspots and faculae and a new structure model for the quietest state of the Sun. We assume that the brightness of the quiet Sun varies in time proportionally to the secular (22-year smoothed) variation of the solar modulation potential. Results. A new reconstruction of the TSI and SSI covering the period 6000 BCE - 2015 CE is presented. The model simulates solar irradiance variability during the satellite era well. The TSI change between the Maunder and recent minima ranges between 3.7 and 4.5 W/m2 depending on the applied solar modulation potential. The implementation of a new quietest Sun model reduces, by approximately a factor of two, the relative solar forcing compared to the largest previous estimation, while the application of updated solar modulation potential increases the forcing difference between Maunder minimum and the present by 25-40 %.
Ground-based whole sky cameras are extensively used for localized monitoring of clouds nowadays. They capture hemispherical images of the sky at regular intervals using a fisheye lens. In this paper, we propose a framework for estimating solar irradiance from pictures taken by those imagers. Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards short-term forecasting of solar energy generation based on cloud movement. We derive and validate our model using pyranometers co-located with our whole sky imagers. We achieve a better performance in estimating solar irradiance and in particular its short-term variations as compared to other related methods using ground-based observations.
Data gaps are ubiquitous in spectral irradiance data, and yet, little effort has been put into finding robust methods for filling them. We introduce a data-adaptive and nonparametric method that allows us to fill data gaps in multi-wavelength or in multichannel records. This method, which is based on the iterative singular value decomposition, uses the coherency between simultaneous measurements at different wavelengths (or between different proxies) to fill the missing data in a self-consistent way. The interpolation is improved by handling different time scales separately. Two major assets of this method are its simplicity, with few tuneable parameters, and its robustness. Two examples of missing data are given: one from solar EUV observations, and one from solar proxy data. The method is also appropriate for building a composite out of partly overlapping records.
The satellite total solar irradiance (TSI) database provides a valuable record for investigating models of solar variation used to interpret climate changes. The 35-year ACRIM TSI satellite composite was updated using corrections to ACRIMSAT/ACRIM3 results derived from recent testing at the Laboratory for Atmospheric and Space Physics/Total solar irradiance Radiometer Facility (LASP/TRF). The corrections lower the ACRIM3 scale by ~5000 ppm, in close agreement with the scale of SORCE/TIM results (solar constant ~1361 W/m^2). Relative variations and trends are not changed. Differences between the ACRIM and PMOD TSI composites, e.g. the decadal trending during solar cycles 21-22, are tested against a set of solar proxy models, including analysis of Nimbus7/ERB and ERBS/ERBE results available to bridge the ACRIM Gap (1989-1992). Our findings confirm: (1) The validity of the TSI peak in the originally published ERB results in early 1979 during solar cycle 21; (2) The correctness of originally published ACRIM1 results during the SMM spin mode (1981-1984); (3) The upward trend of originally published ERB results during the ACRIM Gap; (4) The occurrence of a significant upward TSI trend between the minima of solar cycles 21 and 22 and (5) a decreasing trend during solar cycles 22-23. Our findings do not support: (1) The downward corrections to originally published ERB and ACRIM1 results during solar cycle 21; (2) A step function sensitivity change in ERB results at the end-of-September 1989; (3) the validity of ERBEs downward trend during the ACRIM Gap or (4) the use of ERBE results to bridge the ACRIM Gap. Our analysis provides a first order validation of the ACRIM TSI composite approach and its 0.037%/decade upward trend during solar cycles 21-22. Thus, solar forcing of climate change may be a significantly larger factor than represented in the CMIP5 general circulation climate models.
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