  Solar energy is a clean and renewable energy. Photovoltaic (PV) power is an
important way to utilize solar energy. Accurate PV power forecast is crucial to
the large-scale application of PV power and the stability of electricity grid.
This paper proposes a novel method for short-term photovoltaic power forecast
using deep convolutional long short-term memory (ConvLSTM) network and kernel
density estimation (KDE). In the proposed method, ConvLSTM is used to forecast
the future photovoltaic power and KDE is used for estimating the joint
probabilistic density function and giving the probabilistic confidence
interval. Experiments in an actual photovoltaic power station verify the
effectiveness of the proposed method. Comparison experiments with convolutional
neural network (CNN) and long short-term memory network (LSTM)shows that
ConvLSTM can combine the advantages of both CNN and LSTM and significantly
outperform CNN and LSTM in terms of forecast accuracy. Through further
comparison with other five conventional methods including multilayer perceptron
(MLP), support vector regression (SVR), extreme learning machine (ELM),
classification and regression tree (CART) and gradient boosting decision tree
(GBDT), ConvLSTM can significantly improve the forecast accuracy by more than
20% for most of the five methods and the superiorities of ConvLSTM are further
verified.

Machine Learning;Signal Processing