Search

Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies., Competing Interests: The authors declare no conflict of interest.

Background: Heterosis has been widely used in maize breeding. However, we know little about the heterotic quantitative trait loci and their roles in genomic prediction. In this study, we sought to identify heterotic quantitative trait loci for seedling biomass-related traits using triple testcross design and compare their prediction accuracies by fitting molecular markers and heterotic quantitative trait loci. Results: A triple testcross population comprised of 366 genotypes was constructed by crossing each of 122 intermated B73 × Mo17 genotypes with B73, Mo17, and B73 × Mo17. The mid-parent heterosis of seedling biomass-related traits involved in leaf length, leaf width, leaf area, and seedling dry weight displayed a large range, from less than 50 to ~ 150%. Relationships between heterosis of seedling biomass-related traits showed congruency with that between performances. Based on a linkage map comprised of 1631 markers, 14 augmented additive, two augmented dominance, and three dominance × additive epistatic quantitative trait loci for heterosis of seedling biomass-related traits were identified, with each individually explaining 4.1–20.5% of the phenotypic variation. All modes of gene action, i.e., additive, partially dominant, dominant, and overdominant modes were observed. In addition, ten additive × additive and six dominance × dominance epistatic interactions were identified. By implementing the general and special combining ability model, we found that prediction accuracy ranged from 0.29 for leaf length to 0.56 for leaf width. Different number of marker analysis showed that ~ 800 markers almost capture the largest prediction accuracies. When incorporating the heterotic quantitative trait loci into the model, we did not find the significant change of prediction accuracy, with only leaf length showing the marginal improvement by 1.7%. Conclusions: Our results demonstrated that the triple testcross design is suitable for detecting heterotic quantitative trait loci and evaluating the prediction accuracy. Seedling leaf width can be used as the representative trait for seedling prediction. The heterotic quantitative trait loci are not necessary for genomic prediction of seedling biomass-related traits. [ABSTRACT FROM AUTHOR]

The path towards wind power forecasting has yielded huge socio–economic benefits at a global scale. However, most of the previous studies tend to emphasize the improvement of deterministic forecasting, usually losing sight of the significance of probabilistic forecasting. In this paper, a novel forecasting system that can perform deterministic and probabilistic forecasting of wind power simultaneously, composed by the modules of feature selection, forecasting, system optimization, and system evaluation is presented to further supplement the existing studies in this field. Concretely, a hybrid feature selection strategy is proposed in the feature selection module to determine optimal system input; superior to traditional gradient descent algorithm, a dynamic reservoir theory-based recurrent neural network is developed in the forecasting module; an enhanced multi-objective optimization algorithm with the objectives of accuracy and stability is proposed in the system optimization module to provide an optimal scenario for system parameters; the effectiveness and feasibility of the proposed system is then validated in the evaluation module. Moreover, the comprehensive performance analysis of the proposed system is investigated in depth. Finally, the experimental results demonstrate that the proposed system has a significant advantage over the benchmarks considered, further verifying its tremendous potential to be used in a practical wind power system. [ABSTRACT FROM AUTHOR]

Accurately forecasting power load is essential for utilities to effectively manage their resources, reduce operational costs, and provide improved customer service. However, the current load forecasting lacks the ability to deeply explore data, thus failing to accurately predict both short-term trends and volatility ranges. To address this issue, we construct a novel combined forecasting system based on rough sets, information granulation, deep learning, and multi-objective optimization. In this study, we follow the reasonable granulation criterion for granular computing, which aims to improve the reasonableness and specificity of granular interval prediction under the determination of granularity level, and innovatively propose a novel multi-objective optimization algorithm that can simultaneously constrain the reasonable granulation criterion and theoretically demonstrate the obtained Pareto-optimal solution. Four simulation experiments were conducted using the Australian dataset to evaluate the performance of our proposed system in predicting trend changes and fluctuation ranges of power load. Our results demonstrate that the developed system effectively predicts the trend changes and fluctuation range of power load. Specifically, our system showed a deterministic prediction performance improvement of 13.39% and a granularity interval prediction performance improvement of 6.67% compared to the baseline model. Moreover, we conducted a series of discussion tests to validate the superiority of our system, which further confirmed the effectiveness of our proposed approach. • A multivariate intelligent granularity combination forecasting system (MGCFS) is constructed. • A novel clustering algorithm based on intelligent optimization is presented for information granulation. • Pareto optimal solution is proved theoretically. • A multi- objective swarm intelligent optimization technique is proposed. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a modified EMD-FNN model (empirical mode decomposition (EMD) based feed-forward neural network (FNN) ensemble learning paradigm) is proposed for wind speed forecasting. The nonlinear and non-stationary original wind speed series is first decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EMD technique for a deep insight into the data structure. Then these sub-series except the high frequency are forecasted respectively by FNN whose input variables are selected by using partial autocorrelation function (PACF). Finally, the prediction results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original wind speed series. Further more, the developed model shows the best accuracy comparing with basic FNN and unmodified EMD-based FNN through multi-step forecasting the mean monthly and daily wind speed in Zhangye of China. [Copyright &y& Elsevier]

The rapid development of the photovoltaic industry provides a new source of power for the continued operation of the over-consumed energy world. While providing new opportunities for global energy systems, it also poses challenges for power grids. Therefore, it is a priority to fully grasp the characteristics of photovoltaic changes and accurately forecast and analyze them. To enrich the existing research, a novel hybrid prediction system considering meteorological factors is constructed. First, a feature selection module is introduced to select features and assign weights to exogenous meteorological factors, which breaks through the limitations of single-data dimension prediction. Second, shallow and deep learning models are flexibly applied and multi-objective intelligent optimization strategies are introduced to construct deterministic combinatorial prediction models. The module can effectively increase the diversity of prediction models while fully weighing the accuracy and stability of prediction to meet the needs of different information users. Finally, an interval prediction model is constructed to further enrich the PV power prediction system from the perspective of uncertainty analysis. The empirical study is carried out with 5-minute interval data at three sites, and the results show that the hybrid system obtains superior out-of-sample forecasting performance with technical feasibility and general applicability. • A hybrid system that combines deterministic and uncertainty analysis is built. • The system extends the available data dimensions and improves the predictability. • Flexible application of shallow and deep learning increases model diversity. • Feature selection module is employed for feature selection and weight assignment. [ABSTRACT FROM AUTHOR]

• Propose a novel feature selection method for dimensionality reduction. • Develop an improved multi-objective optimization algorithm. • Establish a forecasting framework based on feature selection and deep learning. • Experimental results demonstrate the effectiveness of the framework. Intelligent financial forecasting modeling plays an important role in facilitating investment-related decision-making activities in financial markets. However, accurate multivariate financial time series forecasting remains a challenge due to its complex nonlinear pattern. Aiming to fill the gap in the field, a novel forecasting framework, based on a two-stage feature selection model, deep learning model, and error correction model, is presented in this study, aiming at effectively capturing the nonlinearity inherent in multivariate financial time series. Concretely, the proposed two-stage feature selection model is utilized to determine the optimal feature set to further improve the generalization of the proposed deep learning model based on three deep learning units. Meanwhile, the error correction model is used to correct the forecasts and improve the accuracy further. To validate the performance of the forecasting framework, the case studies and the corresponding sensitivity analysis are carried out, consequently demonstrating its superiority, as compared to 16 benchmarks considered. [ABSTRACT FROM AUTHOR]

• A suitable PV forecasting framework is successfully established. • Based on deep learning mechanism, the features of time series can be better used. • A pluralistic and scientific evaluation system is constructed. • It is proved theoretically that proposed model can achieve pareto optimal solution. Owing to the continuous increase in the proportion of solar generation accounting for the total global generation, real-time management of solar power has become indispensable. Moreover, accurate prediction of photovoltaic power is emerging as an important link to support grid operations and reflect real-life scenarios. Various studies have led to the design of several forecasting models. Nevertheless, most predictors do not focus on the effects of the factors of photovoltaic modules on the forecast results. To fill this gap, in this paper, a novel multivariable hybrid prediction system combining signal decomposition, artificial intelligence models, deep learning models, and a swarm intelligence optimization strategy is proposed. This system fully utilizes independent variable features (including the module temperature) to efficiently enhance the precision and efficiency of photovoltaic forecasting. In particular, it is proved that a Pareto-optimal solution can be obtained using the designed system. Using three datasets obtained from Safi-Morocco, the presented system is verified by comparative experiments, and its remarkable advantages in terms of forecasting are demonstrated. Specifically, using the three datasets, the symmetric mean absolute percentage errors obtained by the presented forecast system are 2.129%, 2.335%, and 3.654%, respectively, which are significantly lower than those achieved with other comparison models. Furthermore, a comprehensive and rational evaluation methodology is employed to assess the predictive capability of the developed system. The evaluation results show that the system is effective in improving the forecasting efficiency and outperforms other benchmark models. [ABSTRACT FROM AUTHOR]

Accurate wind speed prediction has become increasingly important in wind power generation. However, the lack of efficient data preprocessing techniques and integration strategies has been a big obstacle to the development of wind power forecasting system. Therefore, a novel and advanced combined forecasting system comprising a data preprocessing, an integration strategy and several single models is designed in this study. The proposed model not only eliminates the impact of noise, but also integrates several single-model forecasting results through a weight optimization operator. In addition, the uncertain prediction of wind speed is also discussed in detail. The results show that: (a) The MAPE values of the proposed model are 2.8645%, 2.1843% and 2.8727% respectively for the point prediction. (b) The FICP values of the proposed model are 85.1697, 89.5410 and 88.0111 respectively at the significant level α = 0.05 for the uncertainty forecasting. The AWD values are 0.0559, 0.0400 and 0.0361 and the FINAW values are 0.0478, 0.0404 and 0.0390. It is reasonable to conclude that the proposed system can effectively boost the precision and stability of wind speed forecasting and provide a new approach for the exploitation of wind energy. • A combined forecasting system including a data preprocessing, a combined and uncertainty prediction module is designed. • An advanced data preprocessing technique is intended to remove the noise. • A combined prediction strategy including optimal sub-model selection and weight optimization operators is proposed. • The Pareto optimality of the solutions is theoretically proven. • Interval prediction increases the accuracy and certainty of prediction results. [ABSTRACT FROM AUTHOR]

• A new integrated power load point-interval forecasting system is developed. • A novel multi-objective optimization algorithm is proposed for multi-level optimization. • The proposed forecasting system limits the forecast uncertainty. • To test the proposed model on Australian electricity load data. During an era of rapid growth in electricity demand throughout society, accurate forecasting of electricity loads has become increasingly important to guarantee a stable power supply. Nevertheless, historical models do not address the structure of the data itself, and a single model cannot accurately determine the nonlinear characteristics of the data. This would not allow for accurate and stable predictions. With the aim of filling this gap, this paper proposes an innovative intelligent power load point-interval forecasting system. The system discretizes the time series, then performs efficient dimensionality reduction by fuzzification, and multi-level optimization of five benchmark deep learning models by the proposed multi-objective optimization algorithm, and finally analyzes the uncertainty of the prediction results. Experiments comparing the developed prediction system with other models were conducted on three datasets, and the prediction results were discussed for validation from multiple perspectives. The simulation results show that the proposed model has superior prediction accuracy, robustness and uncertainty analysis capability, and can provide accurate deterministic prediction information and fluctuation interval analysis to ensure the long-term safety and stability and operation of the grid. [ABSTRACT FROM AUTHOR]

• Developed a novel deep QrBiLStm that can perform probabilistic forecasts. • An ensemble probabilistic forecasting system was proposed. • A pseudo-interval training method for ensemble probabilistic forecast is designed. • Improved the optimizer with three strategies. The quantification of wind speed uncertainty is of great significance for real-time control of wind turbines and power grid dispatching. However, the intermittence and fluctuation of wind energy present great challenges in modeling its uncertainty; research in this field is limited. A quantile regression bi-directional long short-term memory network (QrBiLStm) and a novel ensemble probabilistic forecasting strategy are proposed in this study to explore ensemble probabilistic forecasting. To verify the reliability of the proposed ensemble probabilistic forecasting system, the uncertainties of wind speed at wind farms in China were modeled as a case study. The results of comparative experiments including 15 other models demonstrate the superiority of this ensemble probabilistic forecasting system in terms of sharpness while maintaining high interval coverage. More specifically, it was observed that the prediction interval coverage probability obtained by the proposed system is above 97%, and the sharpness is improved by at least 24.21% as compared with the commonly used single models. The proposed ensemble probabilistic forecasting system can accurately quantify the uncertainty of wind speed, and also reduce the operation cost of power systems by improving the efficiency of wind energy utilization. [ABSTRACT FROM AUTHOR]

Abstract: Electric load forecasting is crucial for managing electric power systems economically and safely. This paper presents a new combined model for electric load forecasting based on the seasonal ARIMA forecasting model, the seasonal exponential smoothing model and the weighted support vector machines. The combined model can effectively count for the seasonality and nonlinearity shown in the electric load data and give more accurate forecasting results. The adaptive particle swarm optimization is employed to optimize the weight coefficients in the combined forecasting model. The proposed combined model has been compared with the individual models and the other combined model reported in the literature and its results are promising. [Copyright &y& Elsevier]

• A novel hesitant fuzzy wind speed forecasting system is proposed for the first time. • Multi-fuzzification methods are proposed to deal with the non-determinism problem. • The weights of intervals are determined by multi-objective optimization algorithm. • A new defuzzification model is developed to obtain accurate and reliable forecasts. • The proposed system outperforms comparison models with high accuracy and efficiency. Owing to the nondeterministic nature of wind speed, the conventional fuzzy time series forecasting model has difficulty in establishing a common membership level. Therefore, in this study, the fuzzy series forecasting model was improved based on hesitant fuzzy sets. A hesitant fuzzy wind speed forecasting system with a novel defuzzification method and multiobjective optimization algorithm was developed. First, an advanced decomposition model is employed to extract the effective feature and remove the noise component from the raw wind speed series. Then, the universe of discourse is partitioned into equal and unequal intervals by multifuzzification methods and merged by aggregating hesitant information. A multiobjective intelligent optimization algorithm is applied to determine the optimal weights of different intervals accurately and stably. Furthermore, a novel defuzzification model based on an ordered weighted averaging operator and a regular increasing monotone quantifier is proposed to calculate the final forecasting results. The crucial strengths of the developed system are verifying the possibility of enhancing the performance of wind speed forecasting models by improving conventional fuzzy time series forecasting models and integrating them with decomposition models and artificial-intelligence models. Typical wind speed series datasets with different resolutions were selected to evaluate the performance of the proposed system, and experimental results prove that the proposed system outperforms other comparison models with high forecasting accuracy and computing efficiency. [ABSTRACT FROM AUTHOR]