Data Quality in ANFIS Based Soft Sensors

Jassar, S.; Liao, Zaiyi; Zhao, Lian

doi:10.1007/978-90-481-9419-3_12

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Sensor Models [44] The performance of ANFIS models in forecasting sensor measurements is examined in relation to differences in data quality, such as noise or missing values. The research emphasizes the significance of high-quality data for achieving accurate and trustworthy predictions in soft sensor applications through experiments and studies.…”

Section: Impact Of Data Quality On Predictive Accuracy Of Anfis-based...mentioning

confidence: 99%

A Review: Data Quality Problem in Predictive Analytics

Nugroho

2023

IJAIT

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As data size continues to grow, there has been a revolution in computational methods and statistics to process and analyze data into insight and knowledge. This change in the paradigm of analytical data from explicit to implicit raises the way to extract knowledge from data through a prospective approach to determine the value of new observations based on the structure of the relationship between input and output. Data preparation is a very important stage in predictive analytics. To run quality analytical data, data with good quality is needed in accordance with the criteria. Data quality plays an important role in strategic decision making and planning before the digital computer era. The main challenge faced is that raw data cannot be directly used for analysis. One problem that arises related to data quality is completeness. Missing data is one that often causes data to become incomplete. As a result, predictive analysis generated from these data becomes inaccurate. In this paper we will discuss the problems related to the quality of data in predictive analytics through a literature study from related research. In addition, challenges and directions that might occur in the predictive analytics domain with problems related to data quality will be presented.

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Section: Impact Of Data Quality On Predictive Accuracy Of Anfis-based...mentioning

confidence: 99%

A Review: Data Quality Problem in Predictive Analytics

Nugroho

2023

IJAIT

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“…In ANFIS, the quality of trading and testing data have an important role in determination of soft sensor performance. Training must be performed after extracting desired features from the NF signal [17,18] , thus adaptive noise cancellation method based on the estimation and subtraction of the noise from the signal is used that is one of the most useful methods [19][20][21][22] . Then, trained data without cancelation noise and after noise cancellation of the indoor temperature, will be reformed by suitable track-to-track combination calculation, and then outdoor temperature will be announced via ANFIS and displayed.…”

Section: Anfis Modelmentioning

confidence: 99%

“…These data-driven soft sensors are called adaptive soft sensors and use adaptation methods to avoid model degradation in soft sensors. These soft sensors are called adaptive soft sensors [22,32] . In a research, an inferential sensor based on [7] .…”

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confidence: 99%

Outdoor Temperature Estimation Using ANFIS for Soft Sensors

2019

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In recent years, several studies using smart methods and soft computing in the field of HVAC systems has been provided. In this paper, we propose a framework which will strengthen the benefits of the fuzzy logic and neural fuzzy systems to estimate outdoor temperature. In this regard, ANFIS is used in effective combination of strategic information for estimating the outdoor temperature of the building. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Due to ANFIS accuracy in specialized predictions, it is an effective device to manage vulnerabilities of each experiential framework. The neural fuzzy system can concentrate on measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab and the exhibitions are explored. The aim of this study is to improve the overall performance of HVAC systems in terms of energy efficiency and thermal comfort in the building.

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“…In addition, only a small number of the existing methods have used more than 1000 buildings in a dataset to train the model for good generalization performances. Obviously, the accuracy prediction of machine learning models is dependent on the training algorithm and the quality and quantity of the dataset used to train a model [24]. In choosing the best algorithm among the comparison agorithms for building prediction, it is equally important to evaluate the algorithms on the same dataset, otherwise, it cannot be concluded that one algorithm is better than the other [25].…”

Section: Introductionmentioning

confidence: 99%

Application of Multilayer Extreme Learning Machine for Efficient Building Energy Prediction

et al. 2022

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Building energy efficiency is vital, due to the substantial amount of energy consumed in buildings and the associated adverse effects. A high-accuracy energy prediction model is considered as one of the most effective ways to understand building energy efficiency. In several studies, various machine learning models have been proposed for the prediction of building energy efficiency. However, the existing models are based on classical machine learning approaches and small datasets. Using a small dataset and inefficient models may lead to poor generalization. In addition, it is not common to see studies examining the suitability of machine learning methods for forecasting the energy consumption of buildings during the early design phase so that more energy-efficient buildings can be constructed. Hence, for these purposes, we propose a multilayer extreme learning machine (MLELM) for the prediction of annual building energy consumption. Our MLELM fuses stacks of autoencoders (AEs) with an extreme learning machine (ELM). We designed the autoencoder based on the ELM concept, and it is used for feature extraction. Moreover, the autoencoders were trained in a layer-wise manner, employed to extract efficient features from the input data, and the extreme learning machine model was trained using the least squares technique for a fast learning speed. In addition, the ELM was used for decision making. In this research, we used a large dataset of residential buildings to capture various building sizes. We compared the proposed MLELM with other machine learning models commonly used for predicting building energy consumption. From the results, we validated that the proposed MLELM outperformed other comparison methods commonly used in building energy consumption prediction. From several experiments in this study, the proposed MLELM was identified as the most efficient predictive model for energy use before construction, which can be used to make informed decisions about, manage, and optimize building design before construction.

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Data Quality in ANFIS Based Soft Sensors

Cited by 4 publications

References 11 publications

A Review: Data Quality Problem in Predictive Analytics

A Review: Data Quality Problem in Predictive Analytics

Outdoor Temperature Estimation Using ANFIS for Soft Sensors

Application of Multilayer Extreme Learning Machine for Efficient Building Energy Prediction

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