Short-term Forecasting of Photovoltaic Generation based on Conditioned Learning of Geopotential Fields

Abstract

Due to environmental concerns, renewable energy sources (RES) play an increasingly important role in the energy mix. In France, from 2018 to 2019, an increase of 21.2% and 7.8% of energy production was observed for wind and solar respectively [1]. RES are characterized by high variability and limited predictability, mostly due to their dependence on meteorological factors. This variability presents challenges for RES integration into grids and electricity markets: as the penetration of RES increases, power system balancing becomes more complex, and congestions may occur in the grid. This lack of predictability can also have financial consequences. In some European countries, energy producers have to pay penalties proportional to the forecasting error of the injected power. To address these challenges, it is important to accurately predict the future amount of energy production. In this paper we propose a spot statistical forecasting model for very short-term time horizons (from a few minutes up to 6 hours ahead). This model is based on a combination of heterogeneous inputs with a conditioned learning approach. Spatio-temporal inputs (measurements from geographically distributed PV sites and satellite images) are used to enhance short-term predictability, while a weather analog approach enables adaptability to changes in meteorological conditions by considering the most relevant past observations. The performance evaluations are carried out on a case study featuring nine PV plants located in France, over a one-year period. Index Terms-Short-term solar power forecasting, analog approach, conditional forecast, spatio-temporal, auto-regressive processes, smart grid.

A generic methodology to efficiently integrate weather information in short-term Photovoltaic generation forecasting models

“…It can be implemented in two ways: 1) either through a regime-switching model approach, where each model is dedicated to a specific weather type (e.g. sunny, cloudy) [15,16,17,18] or through binning of weather variables [19], and 2) by taking a dynamic approach, where the model's parameters are updated regularly [14,20,21].…”

“…The first option is the most common approach and considers several sources of ST information: [9,11] use production measurements of spatially distributed PV units, [10,21] consider a selection of pixels derived from satellite imagery, while [23] fits solar forecasting models with observations from nearby irradiance sensors. In [19], the authors combine the idea of WHCO with the use of ST data as explanatory features for 10-second ahead solar forecasts.…”

One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques

“…To tackle the aforementioned challenge, it is essential to improve the performance of windspeed and solar irradiation one-day-ahead forecasting in order to minimize uncertainty about the amount of renewable power that can be generated in any electric grid operational situation. Given the inherent relationship between solar irradiation and the electric power produced from photovoltaics, and between windspeed and wind turbine power generation, it is necessary to create computational models that will accurately predict solar irradiation and windspeed in medium-and/or short-term time scales [5][6][7][8][9][10][11].…”

“…Regarding data-driven models, statistical methods consist of autoregressive integrated moving average (ARIMA) [9][10][11], auto-regressive moving average (ARMA) [12][13][14], Lasso [15], and Markov models [16][17][18]. The most common machine learning methods are support vector machines (SVM) [19][20][21], feed forward neural networks (FFNN) [22], recurrent neural networks (RNN) [23][24][25], convolutional neural networks (CNN) [26,27], long short-term memory networks (LSTM) [28][29][30][31], bidirectional long short-term memory neural networks (BiLSTM) [32], deep belief networks (DBN) [33], and artificial neural networks in general (ANN) [34][35][36].…”

Short-Term Photovoltaic Generation Forecasting Enhanced by Satellite Derived Irradiance

“…To do so, a maximal relevance feature selection (MRFS) scheme is usually implemented: a correlation scores analysis performed between time-lagged satellite-derived information and PV production is used to select pixels having the highest scores. [9,12] use the Pearson correlation score while [13,14] consider the Mutual Information (MI) criterion for its ability to identify non-linear relationships.…”