Wind Power Prediction in View of Ramping Events Based on Classified Spatiotemporal Network

Wind energy has been connected to the power system on a large scale with the advantage of little pollution and large reserves. While ramping events under the influence of extreme weather will cause damage to the safe and stable operation of power system. It is significant to promote the consumption of renewable energy by improving the power prediction accuracy of ramping events. This paper presents a wind power prediction model of ramping events based on classified spatiotemporal network. Firstly, the spinning door algorithm builds parallelograms to identify ramping events from historical data. Due to the rarity of ramping events, the serious shortage of samples restricts the accuracy of the prediction model. By using generative adversarial network for training, simulated ramping data are generated to expand the database. After obtaining sufficient data, classification and type prediction of ramping events are carried out, and the type probability is calculated. Combined with the probability weight, the spatiotemporal neural network considering numerical weather prediction data is used to realize power prediction. Finally, the effectiveness of the model is verified by the actual measurement data of a wind farm in Northeast China.

Optimization of STP Innovation Management Mechanisms Driven by Advanced Evolutionary IoT Arithmetic

Since industrialization, manufacturing has been an important pillar of a country’s economic development. Under the dual pressure of the new trend of global manufacturing development and the loss of competitive advantage of manufacturing industry, it is especially important to accelerate the enhancement of national high technology innovation capacity and the optimization of high technology policy innovation management mechanism driven by advanced evolutionary Internet of Things (IoT) arithmetic. The main of this paper thus introduces the effective method of optimization of high technology policy innovation management mechanism driven by advanced evolutionary IoT arithmetic. To study the optimization of high technology policy innovation management mechanism, a conceptual analysis of currently popular information technologies, such as big data technologies, artificial intelligence technologies, and Internet of Things technologies, and an overview of the application of these technologies in microgrids are given. In the paper, all factors are studied using the STP innovation management mechanism-based approach, and finally, all factors are classified into two categories of cause and effect factors by this approach, and the importance of all factors is ranked. Secondly, a wind power prediction algorithm based on data mining technology and an improved algorithm and a PV power prediction algorithm based on a deep neural network were established with the technical support of high-tech information technology such as big data and artificial intelligence. Finally, the majorization of high technology policy innovation management mechanism driven by advanced evolutionary IoT arithmetic is proposed.

Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System

The building integrated semitransparent photovoltaic (BISTPV) system is an emerging technology which replaces the conventional building material envelopes and roof. The performance prediction of the BISTPV system places a vital role in the reduction of the energy consumption in the building. In this work, the artificial neural network (ANN) is used to predict the performance of this system by optimizing the important parameter of the feature selection. The Elman neural network (EN) algorithm, feed forward neural network (FN), and generalized regression neural network model (GRN) are investigated in this study. The performance metrics of the errors are analysed such as the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean square root (MSE). According to the findings, the model behaves consistently at the specified time and place in the experiment. Forecasters utilizing neural network models will have better accuracy if they use techniques like EN, FFN, and GRN having the RMSE of 0.25, 0.37, and 0.45, respectively.

Power Prediction of Combined Cycle Power Plant (CCPP) Using Machine Learning Algorithm-Based Paradigm

Power prediction is important not only for the smooth and economic operation of a combined cycle power plant (CCPP) but also to avoid technical issues such as power outages. In this work, we propose to utilize machine learning algorithms to predict the hourly-based electrical power generated by a CCPP. For this, the generated power is considered a function of four fundamental parameters which are relative humidity, atmospheric pressure, ambient temperature, and exhaust vacuum. The measurements of these parameters and their yielded output power are used to train and test the machine learning models. The dataset for the proposed research is gathered over a period of six years and taken from a standard and publicly available machine learning repository. The utilized machine algorithms are K -nearest neighbors (KNN), gradient-boosted regression tree (GBRT), linear regression (LR), artificial neural network (ANN), and deep neural network (DNN). We report state-of-the-art performance where GBRT outperforms not only the utilized algorithms but also all the previous methods on the given CCPP dataset. It achieves the minimum values of root mean square error (RMSE) of 2.58 and absolute error (AE) of 1.85.

Ultra-Short-Term Wind Power Prediction Based on Bidirectional Gated Recurrent Unit and Transfer Learning

Wind power forecasting (WPF) is imperative to the control and dispatch of the power grid. Firstly, an ultra-short-term prediction method based on multilayer bidirectional gated recurrent unit (Bi-GRU) and fully connected (FC) layer is proposed. The layers of Bi-GRU extract the temporal feature information of wind power and meteorological data, and the FC layer predicts wind power by changing dimensions to match the output vector. Furthermore, a transfer learning (TL) strategy is utilized to establish the prediction model of a target wind farm with fewer data and less training time based on the source wind farm. The proposed method is validated on two wind farms located in China and the results prove its superior prediction performance compared with other approaches.