Wind speed forecasting using deep neural network with feature selection

Optimization Model of Raw Material Selection Process for Complex Industry Based on Improved Sequential Quadratic Programming Algorithm

Raw materials for industrial production mainly come from nature with wide sources and uneven quality, while production requires strictly with the materials’ quality and their quality indicators are very mutual coupling. In actual production, unreasonable combination and formula often exist in the process of raw material selection, impeding the improvement of production efficiency, quality, energy-saving, and emission reduction. Unfortunately, research on this actual issue is currently vacant, so the main object of this paper is to address the selection of raw materials and formulations in actual production. Based on the designed logical combination algorithm and improved SQP algorithm, this study established the batch combination model and formula calculation model from the perspective of optimal combination and formula. Finally, a practical example of actual production is given to demonstrate the utility and application of this research.

Use of State-of-Art Machine Learning Technologies for Forecasting Offshore Wind Speed, Wave and Misalignment to Improve Wind Turbine Performance

One of the most promising solutions that stands out to mitigate climate change is floating offshore wind turbines (FOWTs). Although they are very efficient in producing clean energy, the harsh environmental conditions they are subjected to, mainly strong winds and waves, produce structural fatigue and may cause them to lose efficiency. Thus, it is imperative to develop models to facilitate their deployment while maximizing energy production and ensuring the structure’s safety. This work applies machine learning (ML) techniques to obtain predictive models of the most relevant metocean variables involved. Specifically, wind speed, significant wave height, and the misalignment between wind and waves have been analyzed, pre-processed and modeled based on actual data. Linear regression (LR), support vector machines regression (SVR), Gaussian process regression (GPR) and neural network (NN)-based solutions have been applied and compared. The results show that Nonlinear autoregressive with an exogenous input neural network (NARX) is the best algorithm for both wind speed and misalignment forecasting in the time domain (72% accuracy) and GPR for wave height (90.85% accuracy). In conclusion, these models are vital to deploying and installing FOWTs and making them profitable.

PM2.5 Concentration Prediction Based on Spatiotemporal Feature Selection Using XGBoost-MSCNN-GA-LSTM

With the rapid development of China’s industrialization, air pollution is becoming more and more serious. Predicting air quality is essential for identifying further preventive measures to avoid negative impacts. The existing prediction of atmospheric pollutant concentration ignores the problem of feature redundancy and spatio-temporal characteristics; the accuracy of the model is not high, the mobility of it is not strong. Therefore, firstly, extreme gradient lifting (XGBoost) is applied to extract features from PM2.5, then one-dimensional multi-scale convolution kernel (MSCNN) is used to extract local temporal and spatial feature relations from air quality data, and linear splicing and fusion is carried out to obtain the spatio-temporal feature relationship of multi-features. Finally, XGBoost and MSCNN combine the advantages of LSTM in dealing with time series. Genetic algorithm (GA) is applied to optimize the parameter set of long-term and short-term memory network (LSTM) network. The spatio-temporal relationship of multi-features is input into LSTM network, and then the long-term feature dependence of multi-feature selection is output to predict PM2.5 concentration. A XGBoost-MSCGL of PM2.5 concentration prediction model based on spatio-temporal feature selection is established. The data set comes from the hourly concentration data of six kinds of atmospheric pollutants and meteorological data in Fen-Wei Plain in 2020. To verify the effectiveness of the model, the XGBoost-MSCGL model is compared with the benchmark models such as multilayer perceptron (MLP), CNN, LSTM, XGBoost, CNN-LSTM with before and after using XGBoost feature selection. According to the forecast results of 12 cities, compared with the single model, the root mean square error (RMSE) decreased by about 39.07%, the average MAE decreased by about 42.18%, the average MAE decreased by about 49.33%, but R2 increased by 23.7%. Compared with the model after feature selection, the root mean square error (RMSE) decreased by an average of about 15%. On average, the MAPE decreased by 16%, the MAE decreased by 21%, and R2 increased by 2.6%. The experimental results show that the XGBoost-MSCGL prediction model offer a more comprehensive understanding, runs deeper levels, guarantees a higher prediction accuracy, and ensures a better generalization ability in the prediction of PM2.5 concentration.

Water tariff forecasting models applied to municipal and private companies in the south and southeast regions of Brazil

This paper has as a main goal to evaluate how models of the forecast will work with a group of variables that were selected based only on their correlation with the average tariff variation. Two forecast models are used, the first based on multiple linear regression techniques and the second based on the application of artificial neural networks (perceptron). We intend to use those models to reach the current water tariff based on the historic variation of the charge and the selected variables applied to municipal and private companies that operate water supply and wastewater systems in the South and Southeast regions of Brazil. The subsidiary data for the elaboration of the models were obtained through the National Sanitation Information System (SNIS). The obtained results indicated that the forecasting processes, in both models used, were able to forecast with high accuracy the fees, and guaranteed the maintenance of the surplus for the analyzed systems.