Artificial intelligence in wind speed forecasting: a review

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values

A machine learning-based approach for product maintenance prediction with reliability information conversion

Predictive maintenance (PdM) cannot only avoid economic losses caused by improper maintenance but also maximize the operation reliability of product. It has become the core of operation management. As an important issue in PdM, the time between failures (TBF) prediction can realize early detection and maintenance of products. The reliability information is the main basis for TBF prediction. Therefore, the main purpose of this paper is to establish an intelligent TBF prediction model for complex mechanical products. The reliability information conversion method is used to solve the problems of reliability information collection difficulty, high collection cost and small data samples in the process of TBF prediction based on reliability information for complex mechanical products. The product reliability information is fully mined and enriched to obtain more reliable and accurate TBF prediction results. Firstly, the Fisher algorithm is employed to convert the reliability information to expand the sample, and the compatibility test is also discussed. Secondly, BP neural network is used to realize the final prediction of TBF, and PSO algorithm is used to optimize the initial weight and threshold of BP neural network to avoid falling into local extreme value and improve the convergence speed. Thirdly, the mean-absolute-percentage-error and the Coefficient of determination are selected to evaluate the performance of the proposed model and method. Finally, a case study of TBF prediction for a remanufactured CNC milling machine tool (XK6032-01) is studied in this paper, and the results show that the feasibility and superiority of the proposed TBF prediction metho

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables

Thermal error modelling of a gantry-type 5-axis machine tool using a Grey Neural Network Model

This paper presents a new modelling methodology for compensation of the thermal errors on a gantry-type 5-axis CNC machine tool. The method uses a “Grey Neural Network Model with Convolution Integral” (GNNMCI(1, N)), which makes full use of the similarities and complementarity between Grey system models and artificial neural networks (ANNs) to overcome the disadvantage of applying either model in isolation. A Particle Swarm Optimisation (PSO) algorithm is also employed to optimise the proposed Grey neural network. The size of the data pairs is crucial when the generation of data is a costly affair, since the machine downtime necessary to acquire the data is often considered prohibitive. Under such circumstances, optimisation of the number of data pairs used for training is of prime concern for calibrating a physical model or training a black-box model. A Grey Accumulated Generating Operation (AGO), which is a basis of the Grey system theory, is used to transform the original data to a monotonic series of data, which has less randomness than the original series of data. The choice of inputs to the thermal model is a non-trivial decision which is ultimately a compromise between the ability to obtain data that sufficiently correlates with the thermal distortion and the cost of implementation of the necessary feedback sensors. In this study, temperature measurement at key locations was supplemented by direct distortion measurement at accessible locations. This form of data fusion simplifies the modelling process, enhances the accuracy of the system and reduces the overall number of inputs to the model, since otherwise a much larger number of thermal sensors would be required to cover the entire structure. The Z-axis heating test, C-axis heating test, and the combined (helical) movement are considered in this work. The compensation values, calculated by the GNNMCI(1, N) model were sent to the controller for live error compensation. Test results show that a 85% reduction in thermal errors was achieved after compensation

Tendencias recientes en el pronóstico de series de tiempo financieras usando máquinas de vectores de soporte

Resumen: El pronóstico de las series de tiempo financieras es un área de trabajo intensiva para investigadores y profesionales. En este estudio, analizamos 59 artículos y discutimos sobe el progreso en el análisis de series de tiempo financieras usando máquinas de vectores de soporte. Las principales conclusiones a las que llegamos son: (a) el pronóstico se hace con datos de frecuencia diaria y los estudios con otras frecuencias de tiempo son escasos; (b) la mayoría de los artículos están enfocados en mejorar el proceso de estimación de los parámetros o en el tratamiento previo de las series de tiempo; (c) la mayor parte de los artículos se concentran en el pronóstico de un índice financiero del mercado; (d) los casos experimentales están dispersos, lo que no hace posible comparar entre diferentes estudios.Abstract: Forecasting of financial time series is an intensive working area for researchers and practitioners. In this study, we analyze 59 articles and discuss the progress in financial time series analysis using support vector machines. Our main conclusions are: (a) forecasting is doing in a daily basis and studies in other time scales are scarce; (b) most of works are devoted to improve the parameter estimation process or to preprocessing the time series; (c) most of the work is concerned to forecast market financial index; (d) experimental cases are disperse and it is no possible to compare between different studiesMaestrí

Machining Performance Analysis in End Milling: Predicting Using ANN and a Comparative Optimisation Study of ANN/BB-BC and ANN/PSO

End milling machining performance indicators such as surface roughness, tool wear and machining time are the principally indicators of machine tool industrial productivity, cost and competitiveness. Since accurate predictions and optimisations are necessary for control purposes, new merit-driven approaches are for good results. The aim of this work is two folds: prediction of machining performance for surface roughness, tool wear and machining time with ANN and the optimisation of these performance indicators using the combined models of ANN-BB-BC and ANN-PSO. However, the optimisation platform is hinged on the fuzzy goal programming model, which facilitates comparisons between the performance of the BB-BC and the PSO algorithms. To demonstrate the approach, optimal tool wear and surface roughness were obtained from a fuzzy goal programme, then converted to a bi-objective non-linear programming model, and solved with the BB-BC and the PSO algorithms. The outputs of the artificial neural network (ANN) were integrated with the optimisation models. The effectiveness of the method was ascertained using extensive literature data. Thus, prediction and optimisation of complex end milling parameters was attained using appropriate selection of parameters with high quality outputs, enhanced by precise prediction and optimisation tools in this proposed approach