# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 1 Introduction  
Over the last decade, a rapid growth of the photovoltaic (PV) market has been observed worldwide, and according to the International Energy Agency (IEA) the global PV capacity exceeds 500 GW\({}_{\text{p}}\)[1]. As the PV produced power depends on the weather conditions that are by nature highly uncertain [2], the penetration of such systems in the actual power system asset represents a challenge [3]. The power produced by the PV plants depends on a number of meteorological variables such as solar irradiance, air temperature, cloud variation, wind speed, relative humidity, etc. PV output power forecasting is a challenge in particular in the case of multi-step applications, large databases, noisy measurements, and multiple input-output observations. On the other hand, reliable forecasts allow avoiding penalties to plant managers caused by deviations between the scheduled and the produced power [4]. The forecast accuracy is generally improved by a pre-processing and a post processing of historical and forecasted PV output power [5]. In the literature, numerous PV power forecasting methods have been developed and, with reference to the forecast horizon [6], these can be divided into four types. Very short-term forecasting with a time horizon ranging from a few seconds to some minutes; short-term forecasting up to 48-72 h ahead; medium-term forecasting from a few days to one week ahead; long-term forecasting from a few months to a year or more. Each forecasting horizon has its specific application so that, for example, very short-term forecasters are used for the control and management of PV systems, in the electricity market, for the control of microgrid, etc. Short-term horizons are adopted for the control of powersystem operations, economic dispatch, unit commitment, etc. Medium and long-terms horizon are usually used for the maintenance and the planning of PV plants. Another classification considers one and multi-step-ahead forecasters, and these latter are more prevalent in many applications. With regards to multi-step forecasting, there are three main strategies: the multi-input multi-output strategy, the recursive strategy, and the direct strategy [7]. The major factors that affect the performance of the forecasters used in the prediction of the power produced by PV plants are [8]: the time-horizon and time resolution, the weather conditions, the geographic location, and the availability and quality of the data. With reference to these parameters, different forecasters can be chosen for the particular need: physical methods [9; 10] that are mainly based on the use of numerical weather prediction (NWP) models [11; 12] or satellite images [13] which can be used for developing regional models. In this case, meteorological data from satellite images are often used for long-term forecasting [3]. NWP models are widely used to predict the state of atmosphere up to 15-days ahead [14], and do not need any historical data. These methods can provide a good accuracy, but depend mainly on the stability of the weather conditions. However, the implementation of physical models is in general relatively difficult, as they require a number of parameters and expensive equipment that are not always available in many areas of the world. In addition, for most of the available NWP the first hours of predictions are not particularly useful for solar forecasting [15]. Another option is statistical and probabilistic approaches. These methods include regression models [16], exponential smoothing, autoregressive models (AR), autoregressive moving integrated average (ARIMA), [17], time series ensemble [18], and probabilistic approaches [19; 20; 21]. Statistical approaches are more suitable for short-term forecasting up to one day-ahead. To cope with the problem of non-linearity several attempts were done, for example an extended application of seasonal time series ensemble for PV forecasting can be found in [18].