The proliferation of photovoltaic (PV) power generation in power distribution grids induces increasing safety and service quality concerns for grid operators. The inherent variability, essentially due to meteorological conditions, of PV power generation affects the power grid reliability. In order to develop efficient monitoring and control schemes for distribution grids, reliable forecasting of the solar resource at several time horizons that are related to regulation, scheduling, dispatching, and unit commitment, is necessary. PV power generation forecasting can result from forecasting global horizontal irradiance (GHI), which is the total amount of shortwave radiation received from above by a surface horizontal to the ground. A comparative study of machine learning methods is given in this paper, with a focus on the most widely used: Gaussian process regression (GPR), support vector regression (SVR), and artificial neural networks (ANN). Two years of GHI data with a time step of 10 $\min$ are used to train the models and forecast GHI at varying time horizons, ranging from 10 $\min$ to 4 $\mathrm{h}$. Persistence on the clear-sky index, also known as scaled persistence model, is included in this paper as a reference model. Three criteria are used for in-depth performance estimation: normalized root mean square error (nRMSE), dynamic mean absolute error (DMAE) and coverage width-based criterion (CWC). Results confirm that machine learning-based methods outperform the scaled persistence model. The best-performing machine learning-based methods included in this comparative study are the long short-term memory (LSTM) neural network and the GPR model using a rational quadratic kernel with automatic relevance determination.

With the development of predictive management strategies for power distribution grids, reliable information on the expected photovoltaic power generation, which can be derived from forecasts of global horizontal irradiance (GHI), is needed. In recent years, machine learning techniques for GHI forecasting have proved to be superior to classical approaches. This work addresses the topic of multi-horizon forecasting of GHI using Gaussian process regression (GPR) and proposes an in-depth study on some open questions: should time or past GHI observations be chosen as input? What are the appropriate kernels in each case? Should the model be multi-horizon or horizon-specific? A comparison between time-based GPR models and observation-based GPR models is first made, along with a discussion on the best kernel to be chosen; a comparison between horizon-specific GPR models and multi-horizon GPR models is then conducted. The forecasting results obtained are also compared to those of the scaled persistence model. Four performance criteria and five forecast horizons (10 min, 1 h, 3 h, 5 h, and 24 h) are considered to thoroughly assess the forecasting results. It is observed that, when seeking multi-horizon models, using a quasiperiodic kernel and time as input is favored, while the best horizon-specific model uses an automatic relevance determination rational quadratic kernel and past GHI observations as input. Ultimately, the choice depends on the complexity and computational constraints of the application at hand.