Log In Sign Up

Short-term probabilistic photovoltaic power forecast based on deep convolutional long short-term memory network and kernel density estimation

by   Mingliang Bai, et al.

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 verified.


page 10

page 19

page 20

page 21

page 23


Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches

The high penetration of volatile renewable energy sources such as solar ...

Deep Photovoltaic Nowcasting

Predicting the short-term power output of a photovoltaic panel is an imp...

Short-term Load Forecasting Based on Hybrid Strategy Using Warm-start Gradient Tree Boosting

A deep-learning based hybrid strategy for short-term load forecasting is...

FORECAST-CLSTM: A New Convolutional LSTM Network for Cloudage Nowcasting

With the highly demand of large-scale and real-time weather service for ...

Improving short-term bike sharing demand forecast through an irregular convolutional neural network

As an important task for the management of bike sharing systems, accurat...