Machine learning methods for solar radiation forecasting: A review

Review of photovoltaic power forecasting

http://coimbra.ucsd.edu/publications/papers/2013_Inman_Pedro_Coimbra.pdf

Solar forecasting methods for renewable energy integration

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

/pdf/online-short-term-solar-power-forecasting-1ljgpbshkb.pdf

Online Short-term Solar Power Forecasting

Solar photovoltaic generation forecasting methods: A review

http://coimbra.ucsd.edu/publications/papers/2013_Marquez_Coimbra.pdf

Intra-hour DNI forecasting based on cloud tracking image analysis

http://coimbra.ucsd.edu/publications/papers/2011_Marquez_Coimbra.pdf

Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database

http://coimbra.ucsd.edu/publications/papers/2013_Marquez_Pedro_Coimbra.pdf

Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs

This work presents an alternative metric for evaluating the quality of solar forecasting models. Some conventional approaches use quantities such as the root-mean-square-error (RMSE) and/or correlation coefficients to evaluate model quality. The direct use of statistical quantities to assign forecasting quality can be misleading because these metrics do not convey a measure of the variability of the time-series for the solar irradiance data. In contrast, the quality metric proposed here, which is defined as the ratio of solar uncertainty to solar variability, compares the forecasting error with the solar variability directly. By making the forecasting error to variability comparisons for different time windows, we show that this ratio is essentially a statistical invariant for each forecast model employed, i.e., the ratio is preserved for widely different time horizons when the same time averaging periods are used, and therefore provides a robust way to compare solar forecasting skills. We employ the proposed metric to evaluate two new forecasting models proposed here, and compare their performances with a persistence model.

/pdf/proposed-metric-for-evaluation-of-solar-forecasting-models-5cmijrxtcc.pdf

Proposed Metric for Evaluation of Solar Forecasting Models

This chapter provides an introduction to and overview of the subsequent chapters, which discuss specific solar-forecasting technologies and time horizons. Solar-forecasting methods are classified by technique, time horizon, and application. Advantages and disadvantages of deterministic and stochastic forecasting approaches are laid out and discussed in the context of solar forecasting based on numerical weather prediction, satellite data, and ground measurements. Metrics to evaluate solar-forecasting techniques are then presented and a time horizon–invariant metric is introduced that allows comparing forecast errors across time horizons, geographical regions, and time steps. Finally, the metric is demonstrated with hour-ahead forecasts based on stochastic-learning and satellite cloud-motion vector techniques.

Ricardo Marquez

Papers

Intra-hour DNI forecasting based on cloud tracking image analysis

Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database

Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs

Proposed Metric for Evaluation of Solar Forecasting Models

Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation