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In the context of prominent energy crisis, photovoltaic power (PV) generation has received increasing attention, then accurate PV generation forecasting is crucial for ensuring the smooth operation of power stations. However, existing research is insufficient in comprehensively analyzing the impact of PV generation volatility. To fill the gaps and enhance the prediction accuracy, this paper proposes a new hybrid forecasting method. We first introduce the Locally Weighted Scatterplot Smoothing (LOWESS) method to process the data and enhance the data stability, and use Pearson correlation coefficient (PCC) and Random Forests (RF) for feature selection to improve the quality of input data. Then we use Attention mechanism and Convolutional Neural Network (CNN) layer to optimize Bi-directional Gate Recurrent Unit (BiGRU) model and form a new hybrid model. Finally, based on the Bagging algorithm, we use ensemble learning to further optimize the hybrid BiGRU model to enhance the depth and performance. The proposed method is validated through case analysis results from two different locations, Xuhui District in Shanghai, China and the DKASC area in Alice Springs, Australia. The results demonstrate that, compared with other models, the developed method exhibits exceptional prediction performance and effectively enhances the accuracy of PV generation forecasting. Keywords : photovoltaic power generation; Locally Weighted Scatterplot Smoothing; feature selection; ensemble learning; Bi-directional Gate Recurrent Unit. • LOWESS smoothing technology is used to reduce the variability of PV generation data. • Pearson correlation coefficient and Random Forest are used to for extract feature. • Attention mechanism and CNN layer are used to optimize BiGRU model. • Two BiGRU layers are stacked to improve the depth and prediction performance. • Ensemble learning is introduced to further optimize the hybrid BiGRU model. [ABSTRACT FROM AUTHOR]

Accurate power load prediction plays a key role in reducing resource waste and ensuring stable and safe operations of power systems. To address the problems of poor stability and unsatisfactory prediction accuracy of existing prediction methods, in this paper, we propose a novel approach for short-term power load prediction by improving the sequence to sequence (Seq2Seq) model based on bidirectional long-short term memory (Bi-LSTM) network. Different from existing prediction models, we apply convolutional neural network, attention mechanism, and Bayesian optimization for the improvement of the Seq2Seq model. Moreover, in the data processing stage, we use the random forest algorithm for feature selection, and also adopt the weighted grey relational projection algorithm for holiday load processing to process the data and thereby overcome the difficulty of holiday load prediction. To validate our model, we choose the power load dataset in Singapore and Switzerland as experimental data and compare our prediction results with those by other models to show that our method can generate a higher prediction accuracy. • Develop a novel power load forecasting approach by improving the Seq2Seq model with various advanced algorithms. • Use the Bi-LSTM in the encoder and decoder of Seq2Seq model to enhance information utilization. • Introduce the Attention mechanism in the Seq2Seq model to help the decoder focus on key sequence information. • CNN and BO algorithm are combined with Seq2Seq model to improve effectively model prediction accuracy. • Random forest and WGRP are used for data processing respectively to improve the effectiveness of data processing. [ABSTRACT FROM AUTHOR]

Accurate prediction of photovoltaic power generation is vital to guarantee smooth operation of power stations and ensure users' electricity consumption. As a good forecasting tool, Gated Recurrent Unit method has been widely used in different forecasting areas. However, the existing studies ignore the impact of data fluctuations on prediction accuracy, to fill the gaps and enhance prediction accuracy, several different data smoothing techniques are introduced and compared to reduce fluctuations, Random Forest method is used for feature selection, and RepeatVector layer extended by attribute dimensions and TimeDistributed layer with full connectivity are utilized to optimize the Gated Recurrent Unit model. A real-world case from the photovoltaic power plant in Xuhui District, Shanghai, China, is adopted to evaluate the performance of proposed method. The comparing results with Recurrent Neural Networks and Long Short-Term Memory, and the actual data as well, show that the proposed prediction method can effectively improve the prediction accuracy of photovoltaic power generation. We also use the daily and monthly data of The Desert Knowledge Australia Solar Centre in Australia to investigate whether the proposed method is suitable for short-term or medium and long-term prediction. The results indicate that our method is more appropriate for short-term prediction. • Introducing and comparing different data smoothing technologies to reduce the data fluctuation. • Using Random Forest to extract the characteristics of natural factors. • Adding RepeatVector layer and TimeDistributed layer into the GRU to improve prediction accuracy. • Utilizing the dataset from Shanghai, China and three prediction models to verify the accuracy. • Utilizing dataset from Alice Springs to verify that our method is more suitable for short-term forecasting. [ABSTRACT FROM AUTHOR]

• Taking real-time price as a feature affecting power load. • Selecting features by minimal redundancy maximal relevance. • Distinguishing holiday and non-holiday when selecting the historical load sequence. • Using an improved particle swarm optimization for parameter optimization. Accurate power load forecasting contributes to guaranteeing safe dispatch and stable operation of a power system. As a great forecasting tool, support vector machine is widely used in power load forecasting. However, due to the rapid development of information technology, the prediction result of simple support vector machine is no longer accurate enough to forecast in the smart grid. To enhance the prediction accuracy, this paper makes some improvements on support vector machine, and proposes a hybrid model integrated with intelligent methods for feature selection and parameter optimization. Firstly, real-time price becomes an important influencing factor of power load as people increasingly rely on demand and real-time price to adjust their electricity consumption patterns. Thus, real-time price, together with other factors that affect power load, is taken as a candidate feature, and minimal redundancy maximal relevance is applied to derive informative features from candidate features. Secondly, as for another feature, the historical load sequence, to make its selection more general, this paper employs the weighted gray relation projection algorithm for holidays to be predicted. Finally, second-order oscillation and repulsion particle swarm optimization is used for optimizing parameters of support vector machine. Moreover, the proposed model is tested via simulations on datasets of Singapore. By comparing prediction results of the proposed model, the support vector machine before improvement and other three forecasting models, this paper confirms that the improvements on support vector machine are effective, and the proposed model outperforms the other forecasting models in aspect of accuracy, stability and effectiveness. [ABSTRACT FROM AUTHOR]