Predictability of PV power grid performance on insular sites without weather stations: use of artificial neural networks

📝 Abstract

The official meteorological network is poor on the island of Corsica: only three sites being about 50 km apart are equipped with pyranometers which enable measurements by hourly and daily step. These sites are Ajaccio (seaside), Bastia (seaside) and Corte (average altitude of 486 meters). This lack of weather station makes difficult the predictability of PV power grid performance. This work intends to study a methodology which can predict global solar irradiation using data available from another location for daily and hourly horizon. In order to achieve this prediction, we have used Artificial Neural Network which is a popular artificial intelligence technique in the forecasting domain. A simulator has been obtained using data available for the station of Ajaccio that is the only station for which we have a lot of data: 16 years from 1972 to 1987. Then we have tested the efficiency of this simulator in two places with different geographical features: Corte, a mountainous region and Bastia, a coastal region. On daily horizon, the relocation has implied fewer errors than a naive prediction method based on the persistence (RMSE=1468 Vs 1383Wh/m2 to Bastia and 1325 Vs 1213Wh/m2 to Corte). On hourly case, the results were still satisfactory, and widely better than persistence (RMSE=138.8 Vs 109.3 Wh/m2 to Bastia and 135.1 Vs 114.7 Wh/m2 to Corte). The last experiment was to evaluate the accuracy of our simulator on a PV power grid localized at 10 km from the station of Ajaccio. We got errors very suitable (nRMSE=27.9%, RMSE=99.0 W.h) compared to those obtained with the persistence (nRMSE=42.2%, RMSE=149.7 W.h).

💡 Analysis

The official meteorological network is poor on the island of Corsica: only three sites being about 50 km apart are equipped with pyranometers which enable measurements by hourly and daily step. These sites are Ajaccio (seaside), Bastia (seaside) and Corte (average altitude of 486 meters). This lack of weather station makes difficult the predictability of PV power grid performance. This work intends to study a methodology which can predict global solar irradiation using data available from another location for daily and hourly horizon. In order to achieve this prediction, we have used Artificial Neural Network which is a popular artificial intelligence technique in the forecasting domain. A simulator has been obtained using data available for the station of Ajaccio that is the only station for which we have a lot of data: 16 years from 1972 to 1987. Then we have tested the efficiency of this simulator in two places with different geographical features: Corte, a mountainous region and Bastia, a coastal region. On daily horizon, the relocation has implied fewer errors than a naive prediction method based on the persistence (RMSE=1468 Vs 1383Wh/m2 to Bastia and 1325 Vs 1213Wh/m2 to Corte). On hourly case, the results were still satisfactory, and widely better than persistence (RMSE=138.8 Vs 109.3 Wh/m2 to Bastia and 135.1 Vs 114.7 Wh/m2 to Corte). The last experiment was to evaluate the accuracy of our simulator on a PV power grid localized at 10 km from the station of Ajaccio. We got errors very suitable (nRMSE=27.9%, RMSE=99.0 W.h) compared to those obtained with the persistence (nRMSE=42.2%, RMSE=149.7 W.h).

📄 Content

1 SUBJECT 5: PV SYSTEMS, subsection: 5.1 PV Power Plants

Predictability of PV power grid performance
on insular sites without weather stations: use of
artificial neural networks**

Cyril Voyant1,2, Marc Muselli2*, Christophe Paoli2, Marie, Laure Nivet2 and Philippe Poggi2
1- Hospital of Castelluccio, radiotherapy unit, B.P.85 20177 Ajaccio - France 2- University of Corsica/CNRS UMR SPE 6134, {Rte des Sanguinaires, 20000 Ajaccio/Campus Grimaldi, 20250 Corte} - France *: Corresponding author: phone/fax: +33(0)4 955 241 30/42, marc.muselli@univ-corse.fr **: a part of this research is founded by CTC (Collectivité Territoriale de Corse)

ABSTRACT

The official meteorological network is poor on the island of Corsica: only three sites being about 50 km apart are equipped with pyranometers which enable measurements by hourly and daily step. These sites are Ajaccio (41°55’N and 8°48’E, seaside), Bastia (42°33’N, 9°29’E, seaside) and Corte (42°30’N, 9°15’E average altitude of 486 meters). This lack of weather station makes difficult the predictability of PV power grid performance. This work intends to study a methodology which can predict global solar irradiation using data available from another location for daily and hourly horizon. In order to achieve this prediction, we have used Artificial Neural Network which is a popular artificial intelligence technique in the forecasting domain. A simulator has been obtained using data available for the station of Ajaccio that is the only station for which we have a lot of data: 16 years from 1972 to 1987. Then we have tested the efficiency of this simulator in two places with different geographical features: Corte, a mountainous region and Bastia, a coastal region. On daily horizon, the relocation has implied fewer errors than a “naïve” prediction method based on the persistence (RMSE=1468 Vs 1383Wh/m² to Bastia and 1325 Vs 1213Wh/m² to Corte). On hourly case, the results were still satisfactory, and widely better than persistence (RMSE=138.8 Vs 109.3 Wh/m² to Bastia and 135.1 Vs 114.7 Wh/m² to Corte). The last experiment was to evaluate the accuracy of our simulator on a PV power grid localized at 10 km from the station of Ajaccio. We got errors very suitable (nRMSE=27.9%, RMSE=99.0 W.h) compared to those obtained with the persistence (nRMSE=42.2%, RMSE=149.7 W.h).

2

1. Presentation and issue We present the results of the prediction of global radiation using Artificial Neural Networks (ANN) which are a popular artificial intelligence technique in the forecasting domain [1]. Inspired by biological neural networks, researchers in a number of scientific disciplines are designing ANNs to solve a variety of problems in decision making, optimization, control and obviously prediction [2-3]. In this context, our aim was to answer to the following question: Can we design an ANN of a site for which there is a lot of solar radiation data available and use this ANN to predict a PV power grid performance of another site? We tried to answer to this question with sites located on the island of Corsica (France). The island is characterized by a Mediterranean climate and a hilly terrain. The official meteorological network (from the French Meteorological Organization) is very poor: only three sites being about 50 km apart are equipped with pyranometers and enable measurements by hourly and daily step. These sites are Ajaccio (41°55’N and 8°48’E, seaside), Bastia (42°33’N, 9°29’E, seaside) and Corte (42°30’N, 9°15’E average altitude of 486 meters). In this study, we focus on the prediction of global solar irradiation on a horizontal plane for daily and hourly horizon. These time steps have been chosen according to the electricity supplier (EDF: Electricité De France) who is interested in the estimation of the fossil fuel saving. It is very important for a remote site where electrification can be problematic [4], and for quantifying the solar potential available. Indeed, this is very important both for the power plant implementation and for sizing of PV array. Solar radiation has been measured for a long time, but even today there are many unknown characteristics of its behavior. So, it seems appropriate to develop a prediction methodology using the data available in another location in order to overcome the lack of weather station and the demand for renewable energy source on the island.
2. Physical phenomena There are two approaches that allow quantifying solar radiation: the “physical modeling” based on physical process occurring in the atmosphere and influencing solar radiation [5], and the “statistic solar climatology” mainly based on time series analysis [5]. We have chosen to combine these two methods to improve the quality of prediction. In this work, we have used the physical phenomena in an attempt to overcome the seasonality of the resource. When studying the solar energy on the earth’s surface with time series,

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