Research on Multistep Time Series Prediction Based on LSTM

Purpose: This study aimed to make CO and PM10 prediction models in DKI Jakarta using Long Short-Term Memory (LSTM) with and without meteorological variables, consisting of wind speed, solar radiation, air humidity, and air temperature to see how far these variables affect the model.Methods: The method chosen in this study is LSTM recurrent neural network as one of the best algorithms that perform better in predicting time series. The LSTM models in this study were used to compare the performance between modeling using meteorological factors and without meteorological factors.Result: The results show that the use of meteorological predictors in the CO prediction model has no effect on the model used, but the use of meteorological predictors influences the PM10 prediction model. The prediction model with meteorological predictors produces a smaller RMSE and stronger correlation coefficient than modeling without using meteorological predictors.Novelty: In this paper, a comparison between the prediction model of CO and PM10 has been conducted with two scenarios, modeling with meteorological factors and modeling without meteorological factors. After the comparative analysis was done, it was found that the meteorological variables do not affect the CO index in 5 air quality monitoring stations in DKI Jakarta. It can be said that the level of CO pollutants tends to be influenced by factors other than meteorological factors.

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“…The implementation of LSTM [8] was conducted using the Keras library in Python programming language.…”

Section: Resultsmentioning

confidence: 99%

“…LSTM is an effective neural network model to predict time series [8]. The LSTM architecture is a particular type of RNN introduced by [9] to avoid long-term dependency problems in common RNNs [10].…”

Section: Introductionmentioning

confidence: 99%

CO and PM10 Prediction Model based on Air Quality Index Considering Meteorological Factors in DKI Jakarta using LSTM

Wattimena

Annisa

Sitanggang

2022

SJI

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“…The LSTM is an improved version recurrent neural network (RNN) with added cell state and gates and thus it has the ability to overcome the gradient vanishing problem that the conventional RNN has [35,36]. The LSTM is characterized by the following sets of equations:…”

Section: Lstm Based Load Forecastingmentioning

confidence: 99%

“…Moreover, in recent times, the upgraded version of the recurrent neural network, named the long short term memory (LSTM) model, has been popular for forecasting [35][36][37]. The LSTM operates well where the conventional recurrent network fails with a large scale of sequential input data.…”

Section: Introductionmentioning

confidence: 99%

Equation Based New Methods for Residential Load Forecasting

Alam¹,

Ali²

2020

Energies

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This work proposes two non-linear and one linear equation-based system for residential load forecasting considering heating degree days, cooling degree days, occupancy, and day type, which are applicable to any residential building with small sets of smart meter data. The coefficients of the proposed nonlinear and linear equations are tuned by particle swarm optimization (PSO) and the multiple linear regression method, respectively. For the purpose of comparison, a subtractive clustering based adaptive neuro fuzzy inference system (ANFIS), random forests, gradient boosting trees, and long-term short memory neural network, conventional and modified support vector regression methods were considered. Simulations have been performed in MATLAB environment, and all the methods were tested with randomly chosen 30 days data of a residential building in Memphis City for energy consumption prediction. The absolute average error, root mean square error, and mean average percentage errors are tabulated and considered as performance indices. The efficacy of the proposed systems for residential load forecasting over the other systems have been validated by both simulation results and performance indices, which indicate that the proposed equation-based systems have the lowest absolute average errors, root mean square errors, and mean average percentage errors compared to the other methods. In addition, the proposed systems can be easily practically implemented.

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“…However, it is clearly stated that their prediction accuracy decreases drastically as the time steps increases. In [27], the authors are able to perform up to eighteen steps ahead for multi-step prediction with minimum error rates using their datasets. Their method requires data distribution with strong and clear seasonality variations, where industrial process data usually do not have.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes

et al. 2022

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This paper investigates the challenging fault prediction problem in process industries that adopt autonomous and intelligent cyber-physical systems (CPS), which is in line with the emerging developments of industrial internet of things (IIoT) and Industry 4.0. Particularly, we developed an end-to-end deep learning approach based on a large volume of real-time sensory data collected from a chemical plant equipped with wireless sensors. Firstly, a novel recursive architecture with multi-lookback inputs is proposed to perform autoregression on imbalanced time-series data as a preliminary prediction. In this process, a novel learning algorithm named recursive gradient descent (RGD) is developed for the proposed architecture to reduce cumulative prediction uncertainties. Subsequently, a classification model based on temporal convolutions over multiple channels with decay effect is proposed to perform multi-class classification for fault root cause identification and localization. The overall network is named the cumulative uncertainty reduction network (CURNet), for its superior capacity in reducing prediction uncertainties accumulated over multiple prediction steps. Performance evaluations show that CURNet is able to achieve superior performance especially in terms of fault prediction recall and fault type classification accuracy, compared to the existing techniques.

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Research on Multistep Time Series Prediction Based on LSTM

Cited by 26 publications

References 6 publications

CO and PM10 Prediction Model based on Air Quality Index Considering Meteorological Factors in DKI Jakarta using LSTM

CO and PM10 Prediction Model based on Air Quality Index Considering Meteorological Factors in DKI Jakarta using LSTM

Equation Based New Methods for Residential Load Forecasting

Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes

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