Accurate and timely short-term traffic flow forecasting is a critical component for intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting ...model due to complex non-linear data pattern of traffic flow. Support vector regression (SVR) has been widely employed in non-linear regression and time series prediction problems. However, the lack of knowledge of the choice of hyper-parameters in the SVR model leads to poor forecasting accuracy. In this study, the authors propose a hybrid traffic flow forecasting model combining gravitational search algorithm (GSA) and the SVR model. The GSA is employed to search optimal SVR parameters. Extensive experiments have been conducted to demonstrate the superior performance of the proposal.
Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase ...restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM10 (particles with a diameter of 10μm or less) concentrations and SO2 (sulfur dioxide) concentrations from four air pollution monitoring stations located in Taiyuan, China. The theoretical analysis and experimental results demonstrated that the forecasting accuracy of the proposed model is very promising.
•Incomplete information may affect PM10 and SO2 forecasting accuracy.•The traditional model was established on incomplete information.•Residuals as the error of traditional model contain unstudied and useful knowledge.•We propose a hybrid method using the residuals by Taylor expansion forecasting model.•The proposed hybrid method shows superior accuracy in simulation.
•This study is the first study to estimate the price of digital currency using deep learning together with decomposition method and optimization algorithm.•A novel hybrid digital currency forecasting ...model based on long short-term memory (LSTM) neural network and empirical wavelet transform (EWT) along with cuckoo search (CS) algorithm is proposed for cryptocurrency time series.•The performance of the proposed model is tested for forecasting of cryptocurrency prices such as Bitcoin, Ripple, Digital Cash and Litecoin.•The forecasting performance of the single LSTM has been improved by combining with EWT decomposition method. Also, the CS optimization algorithm has been identified to be highly effective in improving the performance of the EWT-LSTM hybrid model.•The experimental results show that the proposed model can successfully capture nonlinear features of the cryptocurrency time series.
The price forecasting of the digital currencies in the financial market is of great importance, especially after the recent global economic crises. Due to the nonlinear dynamics, which is including inherent fractality and chaoticity of the digital currencies, it is understood from the research conducted by many researchers that a single model is not sufficient in forecasting the digital currencies with very high accuracy. Since the single models used in the forecasting of digital currencies have weaknesses as well as their own strengths, they might not grant the best forecasting achievement in all situations for all the time. A new hybrid-forecasting framework has been proposed in digital currency time-series to minimize this negative situation and increase forecasting achievement. In this study, a novel hybrid forecasting model based on long short-term memory (LSTM) neural network and empirical wavelet transform (EWT) decomposition along with cuckoo search (CS) algorithm is developed for digital currency time series. The model is obtained by combining the LSTM neural network and EWT decomposition technique, and optimizing the intrinsic mode function (IMF) estimated outputs with CS. The price of the four most traded digital currencies such as BTC, XRP, DASH and LTC, is estimated by the proposed model and the performance of the model has been tested. The experimental results show that the hybrid model proposed for digital currency forecasting can capture nonlinear properties of digital currency time series.
Wind speed forecasting is a promising solution to improve the efficiency of energy utilization. In this study, a novel hybrid wind speed forecasting model is proposed. The whole modeling process of ...the proposed model consists of three steps. In stage I, the empirical wavelet transform method reduces the non-stationarity of the original wind speed data by decomposing the original data into several sub-series. In stage II, three kinds of deep networks are utilized to build the forecasting model and calculate prediction results of all sub-series, respectively. In stage III, the reinforcement learning method is used to combine three kinds of deep networks. The forecasting results of each sub-series are combined to obtain the final forecasting results. By comparing all the results of the predictions over three different types of wind speed series, it can be concluded that: (a) the proposed reinforcement learning based ensemble method is effective in integrating three kinds of deep network and works better than traditional optimization based ensemble method; (b) the proposed ensemble deep reinforcement learning based wind speed prediction model can get accurate results in all cases and provide the best accuracy compared with sixteen alternative models and three state-of-the-art models.
•The EWT method is used to preprocess the original wind speed data.•Reinforcement learning is used to combine three kinds of deep learning networks.•A new ensemble deep reinforcement learning model is proposed to predict wind speed.•The proposed model is compared with nineteen mainstream forecasting models.
•An EV charging demand forecasting model with big data technologies is proposed.•The forecasting model uses historical real-world traffic data and weather data.•A battery charging starting time is ...determined by real-world traffic patterns.•The presented model considers charging demand for electric cars and buses.•The proposed model considers both slow and fast charging classifications.
This paper presents a forecasting model to estimate electric vehicle charging demand based on big data technologies. Most previous studies have not considered real-world traffic distribution data and weather conditions in predicting the electric vehicle charging demand. In this paper, the historical traffic data and weather data of South Korea were used to formulate the forecasting model. The forecasting processes include a cluster analysis to classify traffic patterns, a relational analysis to identify influential factors, and a decision tree to establish classification criteria. The considered variables in this study were the charging starting time determined by the real-world traffic patterns and the initial state-of-charge of a battery. Example case studies for electric vehicle charging demand during weekdays and weekends in summer and winter were presented to show the different charging load profiles of electric vehicles in the residential and commercial sites. The presented forecasting model may allow power system engineers to anticipate electric vehicle charging demand based on historical traffic data and weather data. Therefore, the proposed electric vehicle charging demand model can be the foundation for the research on the impact of charging electric vehicles on the power system.
The continuous development of industry big data technology requires better computing methods to discover the data value. Information forecast, as an important part of data mining technology, has ...achieved excellent applications in some industries. However, the existing deviation and redundancy in the data collected by the sensors make it difficult for some methods to accurately predict future information. This article proposes a semisupervised prediction model, which exploits the improved unsupervised clustering algorithm to establish the fuzzy partition function, and then utilize the neural network model to build the information prediction function. The main purpose of this article is to effectively solve the time analysis of massive industry data. In the experimental part, we built a data platform on Spark, and used some marine environmental factor datasets and UCI public datasets as analysis objects. Meanwhile, we analyzed the results of the proposed method compared with other traditional methods, and the running performance on the Spark platform. The results show that the proposed method achieved satisfactory prediction effect.
•A novel two-stage forecasting architecture is proposed for wind power forecasting.•Considering error factor in wind power forecasting to improve model’s performance.•A novel ensemble method is ...proposed in the proposed forecasting model.•The developed model can also perform better for wind power interval prediction.
With the fast growth of wind power penetration into the electric grid, wind power forecasting plays an increasingly significant role in the secure and economic operation of power systems. Although there have been numerous studies concerning wind power forecasting, most of them have failed to make the best of the information implied in the error value, focused only on simple error correction, adopted a simple ensemble method to aggregate the predictions of each component, and considered improving only forecasting accuracy. Recognizing these issues, a novel two-stage forecasting model based on the error factor, a nonlinear ensemble method and the multi-objective grey wolf optimizer algorithm is proposed for wind power forecasting. More specially, in stage I, the extreme learning machine optimized by the multi-objective grey wolf optimizer is used to forecast the components decomposed by variational mode decomposition, and an error prediction model based on the extreme learning machine optimized by the multi-objective grey wolf optimizer is utilized to predict forecast errors; also, a novel nonlinear ensemble method based on the extreme learning machine optimized by the multi-objective grey wolf optimizer is utilized to integrate all the components and forecast error values in stage II. Three real-world wind power datasets collected from Canada and Spain are introduced to demonstrate the forecasting performance of the developed model. The forecasting results reveal that the proposed model is superior to all the other considered models in terms of both accuracy and stability and thus can be a useful tool for wind power forecasting.
Wind energy prediction has a significant effect on the planning, economic operation and security maintenance of the wind power system. However, due to the high volatility and intermittency, it is ...difficult to model and predict wind power series through traditional forecasting approaches. To enhance prediction accuracy, this study developed a hybrid model that incorporates the following stages. First, an improved complete ensemble empirical mode decomposition with adaptive noise technology was applied to decompose the wind energy series for eliminating noise and extracting the main features of original data. Next, to achieve high accurate and stable forecasts, an improved wavelet neural network optimized by optimization methods was built and used to implement wind energy prediction. Finally, hypothesis testing, stability test and four case studies including eighteen comparison models were utilized to test the abilities of prediction models. The experimental results show that the average values of the mean absolute percent errors of the proposed hybrid model are 5.0116% (one-step ahead), 7.7877% (two-step ahead) and 10.6968% (three-step ahead), which are much lower than comparison models.
•Propose a novel hybrid forecasting model based on multi-objective optimization.•The proposed model is superior to 18 comparison models for wind power prediction.•The proposed hybrid model demonstrates higher prediction accuracy and reliability.•Hypothesis testing is used to make a comprehensive evaluation for proposed model.
•A detailed data processing will make more accurate results prediction.•Taking a full account of more load factors to improve the prediction precision.•Improved BP network obtains higher learning ...convergence.•Genetic algorithm optimized by chaotic cat map enhances the global search ability.•The combined GA–BP model improved by modified additional momentum factor is superior to others.
This paper proposes an appropriate combinational approach which is based on improved BP neural network for short-term gas load forecasting, and the network is optimized by the real-coded genetic algorithm. Firstly, several kinds of modifications are carried out on the standard neural network to accelerate the convergence speed of network, including improved additional momentum factor, improved self-adaptive learning rate and improved momentum and self-adaptive learning rate. Then, it is available to use the global search capability of optimized genetic algorithm to determine the initial weights and thresholds of BP neural network to avoid being trapped in local minima. The ability of GA is enhanced by cat chaotic mapping. In light of the characteristic of natural gas load for Shanghai, a series of data preprocessing methods are adopted and more comprehensive load factors are taken into account to improve the prediction accuracy. Such improvements facilitate forecasting efficiency and exert maximum performance of the model. As a result, the integration model improved by modified additional momentum factor gets more ideal solutions for short-term gas load forecasting, through analyses and comparisons of the above several different combinational algorithms.
The cement industry is a significant contributor to anthropogenic CO2. For China, the cement industry is crucial for development, considering the surging urbanization. CO2 emissions from the industry ...are detrimental to the planet, its ecosystem, and inhabitants. Forecasting of the emissions is a critical step in the emissions' mitigation strategies, and to achieve sustainable development. However, the level of uncertainty accompanying CO2 estimates leads to discrepancies in predictions. The current work aims to study the estimation of cement industry CO2 emissions from an uncertainty-driven technical perspective, and present a forecast for the Chinese cement industry emissions using a novel grey prediction model. By modeling the framework of China's cement industry and the CO2 emissions estimation techniques as grey systems with partially known information, this study develops an interval grey number-based approach to calculate the relative uncertainty. A grey sequence is generated from the whitenization of the interval grey numbers to represent annual emissions from different sources. The proposed approach is more flexible than the conventional midpoint estimate-based approach recommended by JCGM. The proposed model, V-GM(1,N), is found to give the highest accuracy of 97.29% in simulating the actual cement industry CO2 emissions data from 2005 to 2018. Comparative analysis of the proposed model with other forecasting models revealed the superiority of the model. The proposed framework, involving the forecasting model and uncertainty analysis approach, is likely to facilitate the decision-makers in making realistic and reliable forecasts at reasonable computational costs.
•A significant share of China's CO2 emissions can be attributed to its cement industry.•The proposed model can predict the cement industry emissions with about 97% accuracy.•The study pioneers Uncertainty Analysis of the CO2 Emissions Estimates using Grey Numbers.•The study is a pioneer in forecasting CO2 emissions from cement industry using grey forecasting models.•China's cement industry emissions are likely to stabilize after crossing 1700 Mt.
PDF
