Very-Short-Term Power Prediction for PV Power Plants Using a Simple and Effective RCC-LSTM Model Based on Short Term Multivariate Historical Datasets

Abstract

Improving the accuracy of very-short-term (VST) photovoltaic (PV) power generation prediction can effectively enhance the quality of operational scheduling of PV power plants, and provide a reference for PV maintenance and emergency response. In this paper, the effects of different meteorological factors on PV power generation as well as the degree of impact at different time periods are analyzed. Secondly, according to the characteristics of radiation coordinate, a simple radiation classification coordinate (RCC) method is proposed to classify and select similar time periods. Based on the characteristics of PV power time-series, the selected similar time period dataset (include power output and multivariate meteorological factors data) is reconstructed as the training dataset. Then, the long short-term memory (LSTM) recurrent neural network is applied as the learning network of the proposed model. The proposed model is tested on two independent PV systems from the Desert Knowledge Australia Solar Centre (DKASC) PV data. The proposed model achieving mean absolute percentage error of 2.74–7.25%, and according to four error metrics, the results show that the robustness and accuracy of the RCC-LSTM model are better than the other four comparison models.

An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning

“…In order to highlight the powerful non-linear fitting ability, Since the amount of source training data is relatively large relative to the amount of task data, and the amount of data to be calculated is small, in order to test whether the amount of task data affects the migration result when the source training data is migrated based on the maximum mean difference contribution coefficient method, auxiliary sample data is introduced and set are 10 auxiliary sample batches, and the data of August 22, August (22)(23), August (22)(23)(24),..., August (22)(23)(24)(25)(26)(27)(28)(29)(30)(31) are taken as 10 sample data, the number of samples is 96,192,...,960 in sequence. Then take the data under different auxiliary samples as the target data, migrate data close to the target data distribution from the source data, calculate the MMD value of each auxiliary sample, the source data, and the migrated data respectively, and use the migration data of each auxiliary sample The TDBN-DNN model obtained after finetuning the network calculates the target task data, and the Gaussian kernel width control parameter = 2.…”

“…However, the application research of combining it with the related problems of power system is very limited. The authors in [22] introduced deep learning technology to the calculation of wind power and photovoltaic power, and achieved good calculation results. The authors in [23] combined variational modal decomposition and particle swarm optimization with deep confidence networks to calculate short-term electricity consumption.…”

Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

“…Of all the renewable energy sources, solar photovoltaic (PV) energy is considered one of the best options for generating clean energy [1,2]. This type of technology is free of the polluting emissions that cause the greenhouse effect [3].…”

Stand-Alone Hybrid Power Plant Based on SiC Solar PV and Wind Inverters with Smart Spinning Reserve Management

“…However, renewable generation cannot work as an effective active power reserve without reliable renewable energy forecasting methods. Much research is being done in this field, using LSTM (Long Short-Term Memory) neural networks [24,25], RBF (Radial Basis Function) neural networks [26], machine learning techniques [27] or hybrid methods [28][29][30]. In [31,32], instead of a diesel generator, a BESS was used to generate the nominal frequency and make the system frequency independent of the mechanical inertia of a synchronous generator.…”