Performance Comparison of Bayesian Deep Learning Model and Traditional Bayesian Neural Network in Short-Term PV Interval Prediction

Abstract

The intermittence and fluctuation of renewable energy bring significant uncertainty to the power system, which enormously increases the operational risks of the power system. The development of efficient interval prediction models can provide data support for decision making and help improve the economy and reliability of energy interconnection operation. The performance of Bayesian deep learning models and Bayesian shallow neural networks in short-term interval prediction of photovoltaic power is compared in this study. Specifically, an LSTM Approximate Bayesian Neural Network model (ABNN-I) is built on the basis of the deep learning and Monte Carlo Dropout method. Meanwhile, a Feedforward Bayesian Neural Network (ABNN-II) model is introduced by Feedforward Neural Network and the Markov Chain Monte Carlo method. To better compare and verify the interval prediction capability of the ABNN models, a novel clustering method with three-dimensional features which include the number of peaks and valleys, the average power value, and the non-stationary measurement coefficient is proposed for generating sunny and non-sunny clustering sets, respectively. Results show that the ABNN-I model has an excellent performance in the field of photovoltaic short-term interval forecasting. At a 95% confidence level, the interval coverage from ABNN-I to ABNN-II can be increased by up to 3.1% and the average width of the interval can be reduced by 56%. Therefore, with the help of the high computational capacity of deep learning and the inherent ability to quantify uncertainty of the interval forecast from Bayesian methods, this research provides high-quality interval prediction results for photovoltaic power prediction and solves the problem of difficult modeling for over-fitting that exists in the training process, especially on the non-sunny clustering sets.

An Ensemble Deep Learning Model for Provincial Load Forecasting Based on Reduced Dimensional Clustering and Decomposition Strategies

“…Long Short-Term Memory (LSTM) [55] is a type of recurrent neural network model used for processing sequence data such as text, speech, and time series data. The advantage of LSTM models is that they can capture long-term dependencies, which traditional recurrent neural network models cannot achieve.…”

A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM

“…Jiang et al present a new approach to improve the performance of non-invasive load monitoring (NILMI) based on reinforcement learning (RL) [4]. K. Y. Wang et al examined the effectiveness of Bayesian shallow neural networks with Bayesian deep learning models for predicting solar power generation over short time intervals [5]. To increase the prediction's precision, Wu et al proposed an information model similar to a daily clustered convolutional neural network (CNN) to predict PV power generation [6].…”