Deep photovoltaic nowcasting

Abstract

Predicting the short-term power output of a photovoltaic panel is an important task for the efficient management of smart grids. Short-term forecasting at the minute scale, also known as nowcasting, can benefit from sky images captured by regular cameras and installed close to the solar panel. However, estimating the weather conditions from these images-sun intensity, cloud appearance and movement, etc.-is a very challenging task that the community has yet to solve with traditional computer vision techniques. In this work, we propose to learn the relationship between sky appearance and the future photovoltaic power output using deep learning. We train several variants of convolutional neural networks which take historical photovoltaic power values and sky images as input and estimate photovoltaic power in a very short term future. In particular, we compare three different architectures based on: a multi-layer perceptron (MLP), a convolutional neural network (CNN), and a long short term memory (LSTM) module. We evaluate our approach quantitatively on a dataset of photovoltaic power values and corresponding images gathered in Kyoto, Japan. Our experiments reveal that the MLP network, already used similarly in previous work, achieves an RMSE skill score of 7% over the commonly-used persistence baseline on the 1-minute future photovoltaic power prediction task. Our CNN-based network improves upon this with a 12% skill score. In contrast, our LSTM-based model, which can learn the temporal dependencies in the data, achieves a 21% RMSE skill score, thus outperforming all other approaches.

Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant

“…For instance LSTM and CNN algorithms were shown to outperform MLP on nowcasting ( f h = 1 min) using sky images as additional input. The maximum reported skill score was 0.21 against persistence [35]. Using sky camera features, prediction of 10 to 30 min ahead was reported with a forecast skill up to 0.43 [36].…”

Study of the Wind Speed Forecasting Applying Computational Intelligence

“…By definition nowcasting refers to short lead time weather forecasts, the US National Weather Service specifies zero to 3 h, though forecasts up to 6 h may be called nowcasts by some agencies [33]. Nowcasting is critical when managing operations of the smart grid, such as system integration, ensuring power continuity and managing ramp rates [34]. In this chapter, nowcasting refers to short-term wind speed forecasting 6 h ahead.…”

A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand

“…As the arrival of the era of big data, deep learning has been greatly developed. Scholars have proposed several time-series prediction methods based on in-depth learning, including Long Short-Term Memory (LSTM) [32,33], the Recurrent Neural Network (RNN) [34,35], and other prediction methods based on deep learning [36,37], which are used to forecast photovoltaic power generation, energy demand, and power load [36,[38][39][40][41]. Liu et al (2016) established the Gated Recurrent Unit prediction model (GRU) and forecasted China's primary energy demand [40].…”