PV System Performance Evaluation by Clustering Production Data to Normal and Non-Normal Operation

The most common method for assessment of a photovoltaic (PV) system performance is by comparing its energy production to reference data (irradiance or neighboring PV system). Ideally, at normal operation, the compared sets of data tend to show a linear relationship. Deviations from this linearity are mainly due to malfunctions occurring in the PV system or data input anomalies: a significant number of measurements (named as outliers) may not fulfill this, and complicate a proper performance evaluation. In this paper a new data analysis method is introduced which allows to automatically distinguish the measurements that fit to a near-linear relationship from those which do not (outliers). Although it can be applied to any scatter-plot, where the sets of data tend to be linear, it is specifically used here for two different purposes in PV system monitoring: (1) to detect and exclude any data input anomalies; and (2) to detect and separate measurements where the PV system is functioning properly from the measurements characteristic for malfunctioning. Finally, the data analysis method is applied in four different cases, either with precise reference data (pyranometer and neighboring PV system) or with scattered reference data (in plane irradiance obtained from application of solar models on satellite observations)

Data analysis for effective monitoring of partially shaded photovoltaic systems

The purpose of this study is the development of an algorithmic tool to automate the process of analyzing monitoring data of partially shaded PV systems. The approach is to compare long-term and high resolution yield data of a partially shaded Photovoltaic (PV) system (investigated PV) with the yield data of an unshaded PV system or the tilted irradiance (reference PV system) and automatically detect the energy loss due to the expected shadow, caused by any surrounding obstacles and distinct it from any additional energy loss due to other malfunctions

Data analysis for effective monitoring of partially shaded photovoltaic systems

The purpose of this study is the development of an algorithmic tool to automate the process of analyzing monitoring data of partially shaded PV systems. The approach is to compare long-term and high-resolution yield data of a partially shaded Photovoltaic (PV) system (investigated PV) with the yield data of an unshaded PV system or the tilted irradiance (reference PV system) and automatically detect the energy loss due to the expected shadow, caused by any surrounding obstacles and distinguish it from any additional energy loss due to other malfunctions

PV System Performance Evaluation by Clustering Production Data to Normal and Non-Normal Operation

The most common method for assessment of a photovoltaic (PV) system performance is by comparing its energy production to reference data (irradiance or neighboring PV system). Ideally, at normal operation, the compared sets of data tend to show a linear relationship. Deviations from this linearity are mainly due to malfunctions occurring in the PV system or data input anomalies: a significant number of measurements (named as outliers) may not fulfill this, and complicate a proper performance evaluation. In this paper a new data analysis method is introduced which allows to automatically distinguish the measurements that fit to a near-linear relationship from those which do not (outliers). Although it can be applied to any scatter-plot, where the sets of data tend to be linear, it is specifically used here for two different purposes in PV system monitoring: (1) to detect and exclude any data input anomalies; and (2) to detect and separate measurements where the PV system is functioning properly from the measurements characteristic for malfunctioning. Finally, the data analysis method is applied in four different cases, either with precise reference data (pyranometer and neighboring PV system) or with scattered reference data (in plane irradiance obtained from application of solar models on satellite observations)

Data analysis for effective monitoring of partially shaded photovoltaic systems

The purpose of this study is the development of an algorithmic tool to automate the process of analyzing monitoring data of partially shaded PV systems. The approach is to compare long-term and high resolution yield data of a partially shaded Photovoltaic (PV) system (investigated PV) with the yield data of an unshaded PV system or the tilted irradiance (reference PV system) and automatically detect the energy loss due to the expected shadow, caused by any surrounding obstacles and distinct it from any additional energy loss due to other malfunctions

Data analysis for effective monitoring of partially shaded photovoltaic systems

The purpose of this study is the development of an algorithmic tool to automate the process of analyzing monitoring data of partially shaded PV systems. The approach is to compare long-term and high-resolution yield data of a partially shaded Photovoltaic (PV) system (investigated PV) with the yield data of an unshaded PV system or the tilted irradiance (reference PV system) and automatically detect the energy loss due to the expected shadow, caused by any surrounding obstacles and distinguish it from any additional energy loss due to other malfunctions

Promoting citizen science in the energy sector: Generation Solar, an open database of small-scale solar photovoltaic installations

Citizen science is becoming an effective approach in building a new relationship between science and society, in which the desire of citizens to participate actively in knowledge production meets the needs of researchers. A citizen science initiative dealing with the development of photovoltaics (PV) is presented. To generate a “responsible” initiative, the research question has been designed collectively from the beginning, involving diverse actors in order to encourage creativity while addressing their interests and concerns. The result has been called Generation Solar. It aims at co-creating an open database of PV installations including their technical characteristics, and an online map for visualizing them. The initiative responds to a clear scientific demand; an important drawback for researchers working on energy modelling and predictions of production lays precisely in the lack of information about these installations’ locations and characteristics. The initiative invites citizens, companies and public institutions with a PV installation to collaborate by providing such data. Data will follow the format of Open Power System Data in order to be fully exploitable by the scientific community and society. The success of the initiative will rely on the capacity to mobilize citizens and register the largest possible number of installations worldwide.This work has been supported by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 787289 (project GRECO)