Abstract
In the present paper, global horizontal irradiance (GHI) is modelled and forecasted at time horizons ranging from 30 min to 48 h, thus covering intrahour, intraday and intraweek cases, using online Gaussian process regression (OGPR) and online sparse Gaussian process regression (OSGPR). The covariance function, also known as the kernel, is a key element that deeply influences forecasting accuracy. As a consequence, a comparative study of OGPR and OSGPR models based on simple kernels or combined kernels defined as sums or products of simple kernels has been carried out. The classic persistence model is included in the comparative study. Thanks to two datasets composed of GHI measurements (45 days), we have been able to show that OGPR models based on quasiperiodic kernels outperform the persistence model as well as OGPR models based on simple kernels, including the squared exponential kernel, which is widely used for GHI forecasting. Indeed, although all OGPR models give good results when the forecast horizon is short-term, when the horizon increases, the superiority of quasiperiodic kernels becomes apparent. A simple online sparse GPR (OSGPR) approach has also been assessed. This approach gives less precise results than standard GPR, but the training computation time is decreased to a great extent. Even though the lack of data hinders the training process, the results still show the superiority of GPR models based on quasiperiodic kernels for GHI forecasting.
Highlights
Over the past few decades, the penetration of distributed generation in the power grid has been gaining momentum due to environmental concerns and increases in global power demand.being able to handle an increased share of fluctuating power generation from intermittent renewable energy sources—in particular, solar photovoltaics—has become a critical challenge for power grid operators
Even at the lowest forecast horizon (30 min), online Gaussian process regression (OGPR) models based on simple kernels give forecasts comparable to those given by the persistence model, while OGPR models based on quasiperiodic kernels already give better forecasts
At the highest forecast horizon (5 h), the persistence model gives normalized root mean square error (nRMSE) ' 1.11 in summer and nRMSE ' 1.50 in winter; for OGPR models based on simple kernels, nRMSE ' 0.55 in summer and nRMSE ' 0.70 in winter; for OGPR models based on quasiperiodic kernels, nRMSE ' 0.30 in summer and nRMSE ' 0.40 in winter
Summary
Over the past few decades, the penetration of distributed generation in the power grid has been gaining momentum due to environmental concerns and increases in global power demand. Various methods can be considered, such as polynomial [6], modified gaussian [7], trigonometric [8], Fourier [9], exponential smoothing [10] or dynamic harmonic regression methods [11] Another approach is to model and forecast raw GHI time series, without any specific preprocessing. The transformations necessary to achieve stationarity may hinder the performance of forecasting algorithms, since the resulting time series exhibit very low correlation [12] It is shown in [13] that artificial intelligence-based models using raw GHI data outperform conventional approaches based on forecasting the clearness index and are able to capture the periodic component of this time series as well as its stochastic variation. The hyperparameters’ estimated values and detailed numerical results can be found in Appendices A and B, respectively
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