forecasting in data mining

Heliyon 4, 133. 2011) proposed a novel unified model for short, medium and long-term for hourly electric energy demand forecasting. Google Scholar, Chen X, Kang C, Tong X, Xia Q, Yang J (2014) Improving the accuracy of bus load forecasting by two-stage bad data identification method. In the whole forecasting process, 168 optimal forecasting values are generated, and 168*5 WIC values are generated at the same time. IEEE Trans. However, real-world optimization problems always involve multiple objectives and so-called multi-objective optimization, which means, in this case, the solutions for a multi-objective problem, which is the main focus of the algorithm, represent the trade-offs between the objectives due to the nature of such problems (Shenfield and Rostami, 2015). Step 7: Determine the count value. Data mining assists in the analysis of future patterns and character, enabling companies to make informed decisions. Your privacy choices/Manage cookies we use in the preference centre. In (Marino et al. (2019). The Study and Application of a Novel Hybrid System for Air Quality Early-Warning. A comparative online sales forecasting analysis: Data mining The first dataset includes AQI hourly concentrations collected from January 1, 2017, to December 31, 2018, in BJ-TJ-HE. Step 3: Calculate the objective function value of each gray wolf individual in the population, sort according to the size of the objective function value, and select the optimal first three individuals as X, X, and X, respectively. Therefore, with consideration of forecast accuracy, hybrid models which combine a new method with artificial intelligence are of great significance in air quality forecasting field (D'Allura et al., 2011). On the contrary, DEGWO-ANFIS has the lowest effectiveness. 115, 2634. The third step involves developing a unified model which forecasts accurately for different time horizons i.e. These 496, 264274. Creative Commons Attribution License (CC BY). What is Data Mining 107, 118128. 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. Based on the reviewed papers, we assume that it is possible to improve the accuracy by applying advanced approaches like soft computing techniques which outperforms naive methods coming from statistical theory. Hao, Y., Tian, C., and Wu, C. (2019). Based on the above analysis, it can be seen that none of the models has been playing the best forecasting performance in the forecasting process, and various hybrid models are needed to make up for the shortcomings of the single hybrid model. 116, 100109. In this study, a novel model selection forecast system was proposed that overcomes the shortcomings of the single hybrid model, which cannot give the optimal results for the forecasting process. The clustering steps are as follows: Step 1: Initialize the membership matrix U with a random number whose value is between 0 and 1, so that it satisfies the constraint in Eq. Int J Photoenergy 14:110, Saleh AI, Rabie AH, Abo-Al-Ez KM (2016) A data mining based load forecasting strategy for smart electrical grids. past power data. TABLE 7. 158, 29222927. WebData mining happens when data professionals dig into large data sets to locate anomalies and patterns in the data. Compared with the optimal hybrid model, the model selection is approximately reduced by 10%. FIGURE 3. The pollutants grading standard according to AAQS is shown in Supplementary Appendix S1. 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. What is Forecasting in Data Mining - Javatpoint In the comparison of various hybrid models, the forecasting performance of MODEGWO-SVM is better than other hybrid models. Energy Convers Manag 95:406413, Saberian A, Hizam H, Razid MAM, Kadir MZAA, Mirzaei M (2014) Modelling and prediction of photovoltaic power output using artificial neural networks. doi:10.1016/j.atmosenv.2016.10.046, Yang, Z., and Wang, J. WebThe primary benefit of data mining is its power to identify patterns and relationships in large volumes of data from multiple sources. In order to eliminate the difference of the order of magnitude of forecasting metric, the MAE, MAE RMSE, MAPE, STDE, U1, and U2 are normalized. The term predictive analytics refers to the use of statistics and modeling techniques to make predictions about future outcomes and performance. 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. Xu, Y., Yang, W., and Wang, J. The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors. People who work in the data mining field use this type of data analysis to help predict the outcome of business decisions such as moves to increase revenue or reduce risk. These standardized performance measures or metrics helps in providing forecast evaluations and benchmarking (Pelland et al. doi:10.1007/978-3-642-25002-6_30, Muzaffar, S., and Afshari, A. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. In 1930, the Mas Valley event in Belgium caused nearly 60 deaths in a week. A few data mining tools and techniques include: Descriptive modeling For instance, the European Environment Agency (EEA) and the European Commission (EC) have launched, in 2017, an online platform that provides information about current air quality situation based on measurements from more than 2,000 air quality monitoring stations across Europe (Akyz and abuk, 2009). Energy Inform 1 Implicitly it determines the distribution of data after mapping to a new feature space. SVM has two very important parameters: c and g. c is the penalty coefficient, that is, tolerance of errors. Kumar, K., Zindani, D., and Davim, J. P. (2019). For example, the best MAPE of BPNN for the forecasting of NO2 in Beijing shown in Figure 6 is 5.82%, and the worst one is 8.94%. Sharma, E. Energy forecasting based on predictive data mining techniques in smart energy grids. Data mining combines statistics, artificial intelligence and machine learning to find patterns, relationships and anomalies in large data 2. Therefore, the prediction of AQI or other pollution indicators is a challenging task. 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. J. Appl. 1 There are three stages of data mining Full size image As can be seen WebImplementasi Time Series Forecasting. Alanazi M, Alanazi A, Khodaei A (2017) Long-term solar generation forecasting. Pollut. Environ. doi:10.1016/j.atmosenv.2008.08.018, Lu, D.-X., Weng, W.-Y., Su, J., Wang, Z.-B., and Yang, X.-J. doi:10.1039/b918972f, PubMed Abstract | CrossRef Full Text | Google Scholar, D'Allura, A., Kulkarni, S., Carmichael, G., Finardi, S., Adhikary, , , Wei, , et al. The air quality data sequence usually has characteristics such as non-stationarity and nonlinearity; thus, the multi-objective optimization algorithm is a suitable choice. 4) Moreover, Table 5 displays each metric of NO2 forecasting among the developed hybrid forecasting system and the single hybrid models. 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. The evaluation results are output. 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. At the end of the forecasting, the WIC value of the testing sample is calculated. Environ. 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%. Additionally, the DA value of MODEGWO-SVM is over 75%, which indicates that the hybrid model can capture future changing trends of PM10. Consequently, it is difficult to find an optimal combination of ANN's parameters that brings the model to the best performance in the practical air pollutant forecasting where MAPE and R2 are unknown. What is forecasting in data mining? - OpenAI Chat GPT3 Mater. Data mining is the application of specific algorithms for extracting patterns from the huge data[23]. This work was supported by Western Project of the National Social Science Foundation of China (Grant No. Environ. 295, 113051. doi:10.1016/j.jenvman.2021.113051. Step 4: Calculate the new U matrix with Eq. In our study, the air pollution degrees were divided into five levels. For the MAPE, there are average reductions between the proposed model and single hybrid model, by approximately 12.48%, 29.12%, 60.00%, and 66.70% in six cities for the hourly NO2 time series forecasting, respectively. Res. Atmos. 2023 BioMed Central Ltd unless otherwise stated. This article has been published as part of Energy Informatics Volume 1 Supplement 1, 2018: Proceedings of the 7th DACH+ Conference on Energy Informatics. In the 1940s, the smog incident in Los Angeles caused many people to have red eyes, pharyngitis, respiratory disease deterioration, and even confusion and pulmonary edema. Table 5 and Figure 3 demonstrate the following: 1) Focusing on Category I, the new proposed model based on model selection realizes excellent results on the eight evaluation indices in the first season forecasting. 2 to generate. *Correspondence: Yuanchang Deng, dengych@mail.sysu.edu.cn; Chen Wang, wangch339@mail.sysu.edu.cn, Artificial Intelligence-Based Forecasting and Analytic Techniques for Environment and Economics Management, View all If the WIC of the ith model is the smallest, the forecasting value of the ith model provides the optimal forecasting value. Among the four models, MODEGWO-SVM and MODEGWO-BPNN have better forecasting performance, with the MODEGWO-SVM obtaining 64.29% optimal forecasting points and the MODEGWO-BPNN obtaining 21.43% optimal points for six cities in Category II. In (Khatib and Elmenreich 2015), authors proposed a generalized regression artificial neural network for predicting hourly solar radiation. In addition, China's environmental supervisors have also issued some plans and programs, including EIA (Environmental Influence Assessment) and Emergency Response for reducing air pollution. The Generalized Regression Neural Network Oracle. The smallest MAPE values of MODEGWO-SVM are 0.92%, 0.94%, 1.36%, and 0.79% for Hengshui, Tangshan, Chengde, and Xingtai PM2.5 forecasting, and the MODEGWO-BPNN obtains the best MAPE (1.08% and 0.85%) value for Shijiazhuang and Handan. (Feng et al., 2015) combined air mass trajectory analysis and wavelet transform and proposed that ANN predicts the daily average concentration of pollutants 2days in advance, improves the accuracy of prediction, and is superior to other models. For example, in the forecasting processing of NO2, the variation range of parameters is [2, 99]. doi:10.1016/j.atmosenv.2008.07.020, Feng, X., Li, Q., Zhu, Y., Hou, J., Lingyan, J., and Wang, J. In addition, air pollution in China is also quite serious. No use, distribution or reproduction is permitted which does not comply with these terms. The accuracy of the testing sample is calculated by using the WIC. Forecasting emerging technologies using data augmentation and Int J Photoenergy. Overall, the proposed model selection forecast system exhibits outstanding performance in data analysis and time series forecasting for air pollutants. A good overview of these techniques can be found in (Suganthi and Samuel 2012). 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. The forecasting result of Shijiazhuang shows that the R2 value of best hybrid modes (MODEGWO-BPNN) is 0.9993, very close to 1, which indicates that there is less difference between forecasting data and actual data, and the forecasting value is basically consistent with the actual value. 23, 665685. Trend Analysis and Forecast of PM2.5 in Fuzhou, China Using the ARIMA Model. Although BoxJenkins Time Series (ARIMA) and MLR models have been applied to air quality forecasting in urban areas, they have limited accuracy owing to their inability to predict extreme events, and they are not applicable when performing long-term prediction and nonlinear sequence prediction. doi:10.1109/CIBCB.2015.7300294, Shanshan, Q., Liu, F., Wang, J., and Sun, B. Step 6: Update the positions of the top three gray wolf individuals X, X, and X. Gayen, S., and Biswas, A., (2021). 8, 103208. Authors in (Saberian et al. For the second forecasting, the 2nd to 841st samples are the training samples, the 842nd to 1009th samples are the testing samples, and the 1010th sample is the forecasting value. However, the fluctuation range of g is small, with most variations ranging from 0 to 1. Ecol. 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. With more and more data available from sources as varied as social media, remote sensors, and increasingly detailed reports of product movement and market activity data mining offers the tools to fully exploit Big Data and (2008). The number of input layers from 1 to 10 increases for three main air pollutants, which means there are 1,008 pieces of sample data on NO2, PM2.5, and PM10; the train-to-verify ratio 5:1 means that 840 pieces of sample data were used as training data for building the ANN model, while 168 pieces of sample data were used as testing data for finding the training-to-testing ratio and parameter of each ANN model (the optimal number of input layers of each model and the number of hidden layers of LSTM and BPNN). 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. When the kernel function is known, it can simplify the difficulty of solving the problem in high-dimensional space. 2014) implemented solar power modelling method using artificial neural networks (ANNs) which includes two neural network structures, namely, general regression neural network (GRNN) and feedforward back propagation (FFBP) to model a PV panel output power. Firstly, in order to avoid the phenomenon in which the population is iteratively reduced to a certain area, the crossover and selection operations of the DE algorithm are used to maintain the diversity of the population. Pythagorean Fuzzy C-Means Clustering Algorithm. WebRequest Info. Received: 19 August 2021; Accepted: 04 October 2021;Published: 15 December 2021. For the hourly PM10 time series for three categories, it can be observed that the model selection forecasting system attains satisfactory results. Definition of the performance metrics. 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. Cybern. The selected factors should possess the traits of representativeness, feasibility, and system. Forecasting errors lead to unbalanced supply-demand, which adversely affects the operational cost, reliability and efficiency. A Multi Objective Approach to Evolving Artificial Neural Networks for Coronary Heart Disease Classification. In order to reduce the losses caused by air pollution, several health and governmental institutions gather and publish data regarding what is known as AQI to inform people about the state of air pollution. In this study, we used the trapezoidal membership to calculate the membership value. Meteorological and Air Quality Forecasting Using the WRFSTEM Model during the 2008 ARCTAS Field Campaign. The reduction was about 62.39% and 76.49% for one-step forecasting and 2.79%, 6.10%, and 19.33% for the three cities at the hourly interval NO2 forecasting in Category I. Crop production assumptions made far in advance can help farmers make the necessary planning for things like storing and marketing. Figure1 presents the flowchart of the proposed forecasting process. Weight reflects the importance of each factor in synthetic evaluation and directly affects the outcome of the evaluation. Technology Innovation 22, 101441. doi:10.1016/j.eti.2021.101441, Davood, N.-K., Goudarzi, G., Taghizadeh, R., Asumadu-Sakyi, A., and Fehresti-Sani, M. (2021). Air Quality Early-Warning System for Cities in China. In this paper, the 13 cities of BJ-TJ-HE are evaluated to develop an early warning indicator for air quality. Cite this article. Mathematical theory proves that three-layer neural network can approximate any non-linear continuous function with arbitrary precision. Sustainable Cities Soc. In a time series, time is often the independent variable and the goal is usually to make a forecast for the future. The process of model selection is as follows: Each model data is divided into 840 training samples, 168 testing samples, and one forecasting value.

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