Predictive Modeling of PV Energy Production: How to Set Up the Learning Task for a Better Prediction?

Abstract

In this paper, we tackle the problem of power prediction of several photovoltaic (PV) plants spread over an extended geographic area and connected to a power grid. The paper is intended to be a comprehensive study of one-day ahead forecast of PV energy production along several dimensions of analysis: i) The consideration of the spatio-temporal autocorrelation, which characterizes geophysical phenomena, to obtain more accurate predictions. ii) The learning setting to be considered, i.e. using simple output prediction for each hour or structured output prediction for each day. iii) The learning algorithms: We compare artificial neural networks, most often used for PV prediction forecast, and regression trees for learning adaptive models. The results obtained on two PV power plant datasets show that: taking into account spatio/temporal autocorrelation is beneficial; the structured output prediction setting significantly outperforms the non-structured output prediction setting; and regression trees provide better models than artificial neural networks.

Multi-Site Photovoltaic Forecasting Exploiting Space-Time Convolutional Neural Network

“…These one year long datasets are also used by other studies to evaluate the forecasting algorithms [13,27,28]. We consider the PV sites having fixed tilt PV modules equal to the latitude.…”

A Hybrid Technique for Day-Ahead PV Generation Forecasting Using Clear-Sky Models or Ensemble of Artificial Neural Networks According to a Decision Tree Approach

“…A crucial part for the design and the implementation of a reliable forecasting algorithm based on ANNs is represented by the input selection. The set of inputs chosen for the PV day-ahead forecasting is the following: The format of days and quarters of hour is chosen to take into account temporal autocorrelations of the target variable, as suggested in [45]. Temperature and wind speed are selected because they are involved in the panel efficiency estimation (see Section 3.1).…”

“…Temperature and wind speed are selected because they are involved in the panel efficiency estimation (see Section 3.1). Humidity is included because it influences temperature and irradiance [46], and it is exploited with interesting results in several literature works ( [19,20,45]). Finally, CC represents a numerical index for the estimation of the sky covering.…”

Multimodal Forecasting Methodology Applied to Industrial Process Monitoring

“…In this regard, such analysis compares the correlation of a target signal with the same signal but with an iterative delay added. It is used to show periodicities and oscillation modes in the analysed signal [24]. As shown in Fig.…”