Visibility Forecasting Using Autoregressive Integrated Moving Average (ARIMA) Models

Salman, Afan Galih; Kanigoro, Bayu

doi:10.1016/j.procs.2021.01.004

Cited by 52 publications

(23 citation statements)

References 12 publications

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“…In reference [13], a statistical model autoregressive integrated moving average (ARIMA) for weather forecast was proposed, the model with the lowest mean square error value was found accurate with MSE value 0.00029, this work also suggests that by reducing training dataset, accuracy decays (mean square error value increases). In reference [14], a study for the monthly mean temperature forecast for 36 months was completed.…”

Section: Introductionmentioning

confidence: 84%

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Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks

Khan

Shaikh

Siddiqui

et al. 2022

International Journal of Photoenergy

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The solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forecast PV electricity. Moreover, the impact of different environmental conditions for all of the algorithms is investigated. Hourly solar PV power forecasting is done to confirm the effectiveness of various models. Data used in this paper is of one entire year and is acquired from a 100 MW solar power plant, namely, Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This paper suggests recurrent neural networks (RNNs) as the best-performing forecasting model for PV power output. Furthermore, the bidirectional long-short-term memory RNN framework delivered high accuracy results in all weather conditions, especially under cloudy weather conditions where root mean square error (RMSE) was found lowest 0.0025, R square stands at 0.99, and coefficient of variation of root mean square error (RMSE) Cv was observed 0.0095%.

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Section: Introductionmentioning

confidence: 84%

“…It is considered that deep learning methods have proved themselves in discussed studies for accurate short term time series forecasting. In few studies [13,14,25,26], statistical models were also highlighted as better performing methods to forecast accurately.…”

mentioning

confidence: 99%

Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks

Khan

Shaikh

Siddiqui

et al. 2022

International Journal of Photoenergy

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“…The parameter of the ARIMA model could be obtained by maximum likelihood estimation or least squares estimation. This algorithm has been widely used in finance, commercial sale prediction and engineering [29][30][31], it has achieved a good prediction results. However, it is very rare to introduce this method into the prediction of deformation change in mechanical problems, which also highlights the importance of our work.…”

Section: Related Workmentioning

confidence: 99%

ARIMA-FEM Method with Prediction Function to Solve the Stress–Strain of Perforated Elastic Metal Plates

Chen

Dai

Zheng

2022

Metals

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Stress analysis and deformation prediction have always been the focuses of the field of mechanics. The accurate force prediction in plate deformation plays important role in the production, processing and performance analysis of materials. In this paper, we propose an ARIMA-FEM method, which can be used to solve some mechanical problems of 2D porous elastic plate. We have given a detailed theory and solving steps of ARIMA-FEM. In addition, three numerical examples are given to predict the stress–strain of thin porous elastic metal plates. This article uses CST, LST and Q4 elements to discrete the rectangular plates, square plates and circle plates with holes. As for variable force prediction, this paper compared with linear regression, nonlinear regression and neural network prediction, and the results show that the ARIMA method has a higher prediction accuracy. Furthermore, we calculate the numerical solution at four mesh scales, and the numerical convergence is consistent with the theoretical convergence, which also shows the effectiveness of our method. The image smoothing algorithm is applied to keep edge information with high resolution, which can more concisely describe the plate internal changes. Finally, the application scope of ARIMA-FEM, model expansion, superconvergence analysis and other issues have been given enlightening views in the discussion section. In fact, this algorithm combined statistics and mechanics. It also reflects the knowledge integration of interdisciplinary and uses it better to serve practical applications.

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“…This experiment showed that ARIMA has the lowest mean square error (MSE) value of 0.00029 and a coefficient of variation value of 0.00315. The more significant number of prediction data in the ARIMA model increases the MSE value [19].…”

Section: Literature Reviewmentioning

confidence: 99%

On the Application of ARIMA and LSTM to Predict Order Demand Based on Short Lead Time and On-Time Delivery Requirements

2021

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Suppliers are adjusting from the order-to-order manufacturing production mode toward demand forecasting. In the meantime, customers have increased demand uncertainty due to their own considerations, such as end-product demand frustration, which leads to suppliers’ inaccurate demand forecasting and inventory wastes. Our research applies ARIMA and LSTM techniques to establish rolling forecast models, which greatly improve accuracy and efficiency of demand and inventory forecasting. The forecast models, developed through historical data, are evaluated and verified by the root mean squares and average absolute error percentages in the actual case application, i.e., the orders of IC trays for semiconductor production plants. The proposed ARIMA and LSTM are superior to the manufacturer’s empirical model prediction results, with LSTM exhibiting enhanced performance in terms of short-term forecasting. The inventory continued to decline significantly after two months of model implementation and application.

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Visibility Forecasting Using Autoregressive Integrated Moving Average (ARIMA) Models

Cited by 52 publications

References 12 publications

Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks

Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks

ARIMA-FEM Method with Prediction Function to Solve the Stress–Strain of Perforated Elastic Metal Plates

On the Application of ARIMA and LSTM to Predict Order Demand Based on Short Lead Time and On-Time Delivery Requirements

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