Abstract
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 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.
Highlights
In the past few years, the higher penetration of renewable energy sources, in particular solar photovoltaics, into power grids, has brought new challenges for grid operators [1,2,3,4,5].As electricity is not easy to store, supply and demand have to be balanced at all times by grid operators
When looking at the normalized root mean square error (nRMSE) values of the machine learning models for all horizons, it can be noticed that the performances of these models were very similar, but the long short-term memory (LSTM) model and the Gaussian process regression (GPR) model based on kRQ-automatic relevance determination (ARD) kernel outperformed the others as the forecast horizon increased
This paper aims to shed light on the use of machine learning to forecast global horizontal irradiance using endogenous data
Summary
In the past few years, the higher penetration of renewable energy sources, in particular solar photovoltaics, into power grids, has brought new challenges for grid operators [1,2,3,4,5].As electricity is not easy to store, supply and demand have to be balanced at all times by grid operators. Due to the intermittent nature of the solar resource, the deployment of photovoltaic (PV) power generation makes the power grid balance more complex to ensure using standard tools [6]. Evolution of the power distribution grid and smart management tools are necessary to alleviate these constraints. It seems necessary to develop new tools that must help to improve the grid observability and management to go along with the grid’s evolution. Tools that allow accurate forecasting of PV power generation at several time horizons are needed to achieve the power grid stability and reliability. Towards this same objective, in the context of the
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