forecasting in data mining

Predicting Weather Forecasting State Based on Data Mining Res. Data mining combines statistics, artificial intelligence and machine learning to find patterns, relationships and anomalies in large data doi:10.1016/j.apr.2017.04.003. Sci. Atmos. These datasets contain energy readings from the smart meter and power output produced by PV. ScienceDirect is a registered trademark of Elsevier B.V. ScienceDirect is a registered trademark of Elsevier B.V. https://doi.org/10.1016/j.gltp.2021.08.008. WebThe primary benefit of data mining is its power to identify patterns and relationships in large volumes of data from multiple sources. According to the results of each experiment, SVM provided the more optimal forecast values for the three main pollutants in the four quarters of 13 cities. 649, 13621380. Table 4 only shows the optimal model and the percentage of optimal forecasting value for three main air pollutants. 8, 10231030. Comparison of ANN (MLP), ANFIS, SVM, and RF Models for the Online Classification of Heating Value of Burning Municipal Solid Waste in Circulating Fluidized Bed Incinerators. Data mining assists in the analysis of future patterns and character, enabling companies to make informed decisions. However, authors have clearly mentioned that the outliers are rejected based on a global view, where extreme values are considered as outliers. The higher the c, the more intolerable the errors and easy to over-fit. WebThe air quality index (AQI) indicates the short-term air quality situation and changing trend of the city, which includes six air pollutants: PM2.5, PM10, CO, NO2, SO2 and O3. 2016), authors implemented an energy load forecasting technique with long short term memory (LSTM). 23, 665685. Crop production prediction involves a huge amount of data, making it a perfect candidate for data mining methods. By using this website, you agree to our This can be done using a variety of methods, including regression analysis, Two variants of LSTM are presented, standard LSTM and the LSTM-based Sequence-to-Sequence (S2S) architecture. Building Environ. Hence, they are usually considered as an important parameter in training the prediction algorithm. (2011). Publication of this article was sponsored by funds of the Smart Grids research group. Indicators 95, 702710. 4) Fault-tolerant ability: ANN will not have a great impact on the global training results after its local or partial neurons are damaged; the system can work normally even when it is damaged locally. To avoid underfitting and overfitting cross validation will be performed. And then multiple artificial neural networks are used to forecast the main air pollutants for each category and find the optimal models. A Hybrid Model for PM 2.5 Forecasting Based on Ensemble Empirical Mode Decomposition and a General Regression Neural Network. The results of two types of hybrid SVMs are shown in Table 8, which displays that the optimum penalty coefficients of SVM corresponding to pollutant forecasting in different cities vary widely. In this step, the membership functions (MFs) corresponding to each index are obtained. IEEE Trans. Sustainable Cities Soc. doi:10.1016/j.procs.2018.10.006, Rivas, E., Santiago, J., Lechon, Y., Martin, F., Ario, A., Pons Izquierdo, J., et al. 2016 IEEE/PES Trans Distrib Conf Expo (T & D):113, Chakhchoukh Y, Panciatici P, Mili L (2011) Electric load forecasting based on statistical robust methods. Step 3: Optimize the parameters of the best forecasting model. The maximum values of NO2, PM2.5, and PM10 were in Hengshui, Baoding, and Zhangjiakou, with values of 215, 402, and 1581g/m3, and the minimum values of the three main air pollutants were in Zhangjiakou, Beijing, and Zhangjiakou, with values of 1, 3, and 12g/m3. Yang pertama adalah windowing untuk mengubah data deret waktu menjadi kumpulan data generik: Langkah ini akan mengubah baris terakhir dari suatu jendela dalam rangkaian waktu menjadi label atau variabel target. volume1, Articlenumber:44 (2018) 2. doi:10.1016/j.egypro.2015.11.796. 138, 3340. Then, to further improve modeling accuracy and rationality of modeling, a modified optimization algorithm (DEGWO) was used to optimize the premasters of different models. What is Data Mining The result of model selection for main air pollutants in different seasons. The outcome of investigation from these steps will explore the interplay between anomaly detection technique and forecasting model accuracy. Long-term Effects of Outdoor Air Pollution on Mortality and MorbidityPrediction Using Nonlinear Autoregressive and Artificial Neural Networks Models. In addition, air pollution in China is also quite serious. 158, 29222927. Energ. The results of Category I indicate that the smaller the MAE and MSE, the smaller the deviation between the observations and forecasting, which verifies the forecasting performance. 14. Crop yield forecasting using data mining - ScienceDirect Weather Prediction Using Data Mining They do this for the purpose of predicting outcomes. Eng. Using quarterly U.S. GDP data from 1976 to 2020 we find that the machine In 1948, the American Donora incident caused 5,911 people to become violent. No use, distribution or reproduction is permitted which does not comply with these terms. Algorithm 1 briefly outlines the process of the MODEGWO. doi:10.1016/j.ecolind.2018.08.032, Zhou, Q., Jiang, H., Wang, J., and Zhou, J. 1) For PM10 forecasting in the first season, the optimal hybrid models are DEGWO-SVM and DEGWO-BPNN, with which the MAPE values of the best hybrid model (DEGWO-SVM) for four cities of Category III are 0.71%, 0.81%, 1.09%, and 0.72%. Therefore, the prediction of AQI or other pollution indicators is a challenging task. Create Mining Structure Use relational data source Choose Microsoft Time Series model Select Data Source View Select key, input and This requires accurate forecasts of future energy production and demand/consumption. In terms of skewness, all data sets are rightward, with values of skewness are greater than 0. Prediction Queries (Data Mining) | Microsoft Learn Air Pollution: A Review and Analysis Using Fuzzy Techniques in Indian Scenario. Multi-objective Spotted Hyena Optimizer: A Multi-Objective Optimization Algorithm for Engineering Problems. An Improved Fuzzy Synthetically Evaluation 163, 214222. Eight performance metrics are applied to assess the performance of the proposed model. doi:10.1016/j.jhazmat.2009.05.029, Bessagnet, B., Couvidat, F., and Lemaire, V. (2019). A large sample of the times series is another reason that the training stability of the neural network can be ensured. 3) The forecasting metric of the single hybrid models and the proposed model in Table 4 indicates that the proposed model based on model selection performs better than the single hybrid model in Category III. Looking back at the previous literature on air quality forecasting research, the shortcomings of the traditional air quality forecasting models are summarized as follows: 1) the large amount of information required by the CTM model leads to uncertainty in the forecasting. Impacts of Haze Pollution on China's Tourism Industry: A System of Economic Loss Analysis. In our study, the weight was calculated by fuzzy weighting method. Various models have been proposed to identify the interactions between various air pollutants and their emission sources (Yang and Wang, 2017). In the whole experiment it can be observed that the support vector machine has good forecasting accuracy for three main air pollutants forecasting, but it cannot provide the best forecasting value in each point. Data Analytics in Weather Forecasting: A Systematic Review The hybrid algorithm not only improves the global search ability but also effectively avoids the defects of early maturity stagnation and falling into local optimum. (2018). We use cookies to help provide and enhance our service and tailor content and ads. Fresh climate and the environmental conditions are the stream past power data. In May 2023, Frontiers adopted a new reporting platform to be Counter 5 compliant, in line with industry standards. The target function value is output. For example, in the forecasting processing of NO2, the variation range of parameters is [2, 99]. Subsequently, the marching process of our developed combined model is demonstrated. Additionally, the values of other forecasting metrics are at their best under the model selection. Some examples of data mining in marketing are: #1) Forecasting Market. The atmosphere is a highly complex dynamic system. Meanwhile, SVM is based on the small sample statistical theory, which conforms to machine learning. Eng. Step 4: Calculate the distance between other gray wolf individuals in the population and the optimal X, X, and X according to Eqs 35. J. Environ. Terms and Conditions, As an example, with respect to Tianjin, the DA values of the individual hybrid models are 80.84% (MODEGWO-SVM), 70.06% (MODEGWO-GRNN), and 77.84% (MODEGWO-BPNN), while the DA values of the proposed models is 87.24%, respectively. TABLE 9. The forecasting approaches which are present in the literature usually utilize proprietary data. Time Series Meteorological Variations of PM2.5/PM10 Concentrations and Particle-Associated Polycyclic Aromatic Hydrocarbons in the Atmospheric Environment of Zonguldak, Turkey. FORECASTING WITH DATA MINING ALGORITHMS Conference: MAS 14th INTERNATIONAL EUROPEAN CONFERENCE ON MATHEMATICS, ENGINEERING, NATURAL It is the biggest urbanized megalopolis region in Northern China, where Beijing, Tianjin, Baoding, and Langfang are the central core areas of BJ-TJ-HE. The idea is to choose an appropriate anomaly detection technique and data-driven methodology for energy production forecasting along with developing a unified model for long-term forecasting with step of short-term (hourly) accuracy. This section focuses on the efficiency of the different forecasting model with respect to computational performance. A flow chart of the hybrid model is presented in Figure 1. Artificial Neural Network Forecasting of PM2.5 Pollution Using Air Mass Trajectory Based Geographic Model and Wavelet Transformation. Furthermore, air quality assessment algorithms are developed to assess air quality and protect human health from air pollution and play a vital role in air quality warning systems. The developed model selection forecasting system was evaluated on hourly NO2, PM2.5, and PM10 from 13 cities, and several performance metrics were calculated, with experimental results indicating that the model selection forecasting system is superior to single hybrid models with the smallest MAPE in the different cities pollutant forecasting, indicating its strong forecasting performance. At the end of the forecasting, the WIC value of the testing sample is calculated. The result of fuzzy comprehensive evaluation is shown in Table 1, which found that the main air pollutants are PM10, PM2, and NO2 in 13 cities. Air Quality Early-Warning System for Cities in China. The data mining application is implemented in Java 2 version 1.4.1. 73. doi:10.1016/j.asoc.2018.08.012, Liu, Y., Guo, H., Mao, G., and Yang, P. (2008). Atmos. Forecasting result of NO2 for three categories in the first season. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Data-driven modelling (DDM) is emerging as another important aspect in forecasting energy production problem. Atmos. Trend Analysis and Forecast of PM2.5 in Fuzhou, China Using the ARIMA Model. The result of two types of SVM for three main air pollutants in different cities. Fig. CSEE J Power Energy Syst 1:3846, Zhang J, Florita A, Hodge B, Lu S, Hamann HF, Banunarayan V, Brockway AM (2015) A suite of metrics for assessing the performance of solar power forecasting. For the DE algorithm and gray wolf optimization (GWO) algorithm, the defects of prematurity, poor stability, and ease in falling into local optimum will occur when solving the optimization problem separately. The evaluation part involves feature extraction and finding out the main air pollutants; in the forecasting part, a new metric is developed to find the optimal model in each category, and optimal forecasting models are optimized with modified gray wolf optimization (DEGWO) optimization algorithm and leave-one-out deciding weight strategy to improve the accuracy of forecasting results and provide support for early warning systems. Forecasting ET based supervised learning. 9:761287. doi: 10.3389/fenvs.2021.761287. 4) The model selection index is used to select the optimal forecasting value from the optimal hybrid model. Energy forecasting based on predictive data mining Finally, the modified multi-objective optimization algorithm is used to optimize the parameters of optimal models and model selection to obtain final forecasting values from optimal hybrid models. Regional Transport, Source Apportionment and Health Impact of PM 10 Bound Polycyclic Aromatic Hydrocarbons in Singapore's Atmosphere. The datasets of hourly concentrations of the six major air pollutants used in this study are retrieved from the website of the China National Environment Monitoring Centre (http://www.cnemc.cn/sssj/). In this paper we aim to exploit the available past power data and to assess the performance of data-driven forecasting model in terms of accuracy by applying data pre-processing techniques. 2) A model selection index is established to select the optimal forecasting model from different neural network models. Int J Photoenergy:113, Liu J, Fang W, Zhang X, Yang C (2015) An improved photovoltaic power forecasting model with the assistance of aerosol index data. Eight evaluation criteria are applied to estimate the forecasting performance, namely, mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), Theil U statistic 1 (U1), and Theil U statistic 2 (U2) were calculated for all the fits; the goodness of forecasting fit (R2) and the standard of forecasting error (STDE) indicates the stability of the forecasting models; and the direction accuracy (DA) evaluates the optimal decision-making, often relying on correct forecasting directions or turning points between the actual and forecasting values. Editors J. K. Mandal, and S. Mukhopadhyay. doi:10.1016/j.scitotenv.2018.08.315, Shenfield, A., and Rostami, S. (2015). Authors in (Gandelli et al. This section mainly discusses the hyperparameter related to the SVM and ANN model that would influence the forecasting performance. Energy forecasting is a technique to predict future energy needs to achieve demand and supply equilibrium. Model. doi:10.1016/j.envsci.2020.10.004, Hao, Y., Niu, X., and Wang, J. Pendekatan RapidMiner terhadap deret waktu didasarkan pada dua proses transformasi data utama. According to Table 5, it is obvious that the values of MAE, RMSE, MAPE, U1, and U2 of the proposed hybrid model are all smaller than the other considered models, and the values of DA and R2 of the developed hybrid forecasting system are all greater than that of the single hybrid model, which further confirms the superiority of the presented hybrid forecasting system in terms of forecasting ability. Compared with the optimal hybrid model, the model selection is approximately reduced by 10%. Analyst 135, 230267. 4) According to the results in Table 6, the three kinds of hybrid models are used to forecast PM10 for four cities of Category III in the fourth season, and the R2 value of each model was greater than 0.99, which shows that these models have a good forecasting performance for the PM10. Then, as the initial population of the GWO algorithm, the objective function value of the individual is calculated. doi:10.1016/j.wasman.2017.03.044, Zhang, L., Lin, J., Qiu, R., Hu, X., Zhang, H., Chen, Q., et al. A Bayesian Hierarchical Model for Urban Air Quality Prediction under Uncertainty. According to the World Bank, China loses 10% of its gross domestic product each year due to air pollution. It is evident that the forecasting capacity of the model selection is robust when considering each forecasting metrics. The forecasting result of each model in different seasons for Category III. Sort the parent population of gray wolves in a nondecreasing order; The hybrid AQI forecasting system in this paper is composed of the above three parts. However, there are some scenarios where the on-site measurements for solar irradiation and other meteorological variables like temperature and humidity are unavailable and only the past power measurements are available. The comparative analysis between the proposed model and the single model confirms the advantages of the hybrid forecasting model. Due to the diversity of pollutants and the fluctuation of single pollutant time series, it is a challenging task to find out the main pollutants and establish an accurate forecasting system in a city. Neural network is used in the field of air pollution to solve the problem of non-linear forecasting which cannot be solved by statistical models. Anfis: Adaptive-Network-Based Fuzzy Inference System. Meteorological and Air Quality Forecasting Using the WRFSTEM Model during the 2008 ARCTAS Field Campaign. (2018) applied the RIMA model to predict the concentration of PM2.5 based on time series air quality data covering two warm periods and two cold periods and concludes that PM2.5 concentration is higher in the cold period and lower in the warm period. Air pollution has become the fourth leading health risk factor for China after smoking, diet, and obesity (Zhang et al., 2018). total Environ. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Environ. The final forecasting values are obtained by model selection; based on the results of DEGWO-SVM and DEGWO-BPNN the MAPE values of model selection are 0.65%, 0.75%, 0.97%, and 0.67%. 148, 239257. Environ. Innovative computing. Create Data Source View. FIGURE 4. Hao, Y., and Tian, C. (2018). Supervised learning uses a set of known categories of samples to optimize the parameters of the classifier, enabling the Crop production assumptions made far in advance can help farmers make the necessary planning for things like storing and marketing. 3) The forecasting performance of the optimal single model is improved. ANN has certain fault-tolerant ability. The process of establishing a fuzzy synthetic evaluation (FSE) system is as follows (Lu et al., 2011). In order to improve the computing efficiency and save the computing time, training and forecasting processing of all the models for the main air pollutants time series with parallel computing by central processing unit (CPU) and graphics processing unit (GPU). Difference Between Descriptive and Predictive Data Mining Although the construction of the combined model is usually based on actual problems to achieve the expected test objectives, there are still some problems that most of the past studies have focused on improving the prediction accuracy of the model while ignoring the stability of the model prediction. A Novel Hybrid Bat Algorithm for Solving Continuous Optimization Problems. Man. 3) According to forecasting results in Table 7 and Figure 5 for PM10 of the third season, the three kinds of hybrid models (DEGWO-SVM, DEGWO-BPNN, and DEGWO-ANFIS) are employed to forecast hourly PM10; the DEGWO-SVM has the best forecasting performance among the three hybrid models in Zhangjiakou, and the MAPE is 0.61%. Data Mining Used in Marketing Despite the fact that data mining is seen as secondary data analysis (Hand 1998) the forecasting problem described in this case study is in fact (at least to large part) a primary data analysis since the case company actively conducts an experiment (the retail test) in order to determine the expected sales potential of their newly introduced products. In order to evaluate the performance of the forecasting algorithm, various performance metrics are available in the literature. If c is too large or too small, the generalization ability becomes worse. Meanwhile, model selection uses the predicted values of each model to form the final forecasting results, and the corresponding MAE values are 0.5747, 0.6459, 0.7297, and 0.6300 for four cities. To integrate RES in the power grid, forecasting photovoltaic (PV) yield is very important, as the output of PV systems is sensitive to weather conditions and to the varying strength of solar irradiance striking the PV surface throughout the day. Proced. A comparative online sales forecasting analysis: Data mining Privacy 2 to generate. The proposed system employed fuzzy C-means cluster algorithm to analyze 13 original AQI series, and fuzzy comprehensive evaluation is used to find out the main air pollutants in each city. Softw. The average reduction of MAPE among the MODEGWO-SVM and the other three hybrid models is 16.70%, 42.79%, and 50.10%, respectively. Inf Control 7:115118, Gandelli A, Grimaccia F, Leva S, Mussetta M, Ogliari E (2014) Hybrid model analysis and validation for PV energy production forecasting. doi:10.1016/j.knosys.2018.03.011, Daz-Robles, L. A., Ortega, J. C., Fu, J. S., Reed, G. D., Chow, J. C., Watson, J. G., et al. At the same time, the other data sets had a thin tail. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Int J Photoenergy. There is a possibility that the training accuracy can be very high, and the test accuracy is not high, that is, over-fitting. Following are the goals of data mining. In (Marino et al. J. Environ. The author declares that she has no competing interests. If it is less than a certain threshold, or if the amount of change from the value of the last value function is less than a certain threshold, the algorithm stops. However, complex models such as deep learning models do have a limitation in terms of interpretability. Secondly to construct the case study they used historical electricity load dataset. The evaluation results are output. The optimal three individuals X, X, and X are selected to update the positions of other gray wolf individuals. 295, 113051. doi:10.1016/j.jenvman.2021.113051. To perform predictions typically larger datasets in connection with deep learning are becoming common. For the unknown samples, the classification effect is very poor. Appl. Sci. The most serious is the well-known London smog event of 1952more than 4,000 deaths in 4days and more than 8,000 deaths in 2months. It is practical to use ANN in real air pollutants forecasting application where forecasting the changing air pollutant time series is suitable. (2019). https://doi.org/10.1186/s42162-018-0048-9, DOI: https://doi.org/10.1186/s42162-018-0048-9. 2018 IEEE Int Energy Conf (ENERGYCON):16, Pelland S, Remund J, Kleissl J, Oozeki T (2013) Brabandere KD (2013) Photovoltaic and solar forecasting: State of the art. Because of this, utilities have to balance supply and demand at every moment. 8, 103208. Due to the diversity of pollutants and the fluctuation of single pollutant time series, it is a challenging task to find out the main pollutants and establish an accurate forecasting system in a city.
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