An energy prediction method using Adaptive Neuro-Fuzzy Inference System and Genetic Algorithms

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)

Carino

et al. 2016

Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which\ud have the potential for causing a great impact on the effectiveness and performance of the overall process and the\ud sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this\ud impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations\ud before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the\ud combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal\ud modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating\ud condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of\ud recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of\ud performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.Postprint (published version

Section: E Comparison With Other Methodsmentioning

confidence: 99%

Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps

Zurita

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)

Carino

et al. 2016

“…• G-ANFIS: This model uses an input selection method based on a genetic algorithm in order to select the most suitable inputs that helps the model to reduce the fixed cost function. According to the literature, the selected cost function is the RMSE of the model with the validation data set [10]. • PCA-ANFIS: For this method, a PCA is used to compress the information of the process with the objective of maintaining the maximum variance among the samples.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…The scientific literature in regard with the proposal of industrial process data forecasting can be divided in three different approaches: (i) increasing the inputs of the model [9] that means to add extra inputs rather than the forecasted signal in order to take advantage of the influences of these inputs to anticipate changes in the signal, (ii) adding other relevant information to the system by using a selection method supported by an optimization technique such as Genetic Algorithm [10], which is a generalization of the previous approach in order to select the best signals regarding a cost function, usually related with some statistical metric over the validation error of the model, or (iii) compressing the information using feature reduction methods such as Principal Component Analysis [11] or Linear Discriminant Analysis [12], in order to reduce the amount of inputs of the forecasting model by a previous information compression stage.…”

Section: Introductionmentioning

confidence: 99%

Time series forecasting by means of SOM aided Fuzzy Inference Systems

Zurita

Carino

2015 IEEE International Conference on Industrial Technology (ICIT)

et al. 2015

The forecast of industrial process time series represents a critical factor in order to assure a proper operation of the whole manufacturing chain, as it allows to act against any process deviation before it affects the final manufactured product. In this paper, in order to take advantage from process relations and improve forecasting performance, a prediction method based in Adaptive Neuro Fuzzy Inference System (ANFIS) and Self-Organizing Maps is presented. The novelties of the proposed method are based on considering, as an input of an ANFIS model, the interrelations of process variables regarding the signal that wants to be forecasted, by means of topology preservation SOM. An experimental study performed with real industrial data from a cooper manufacturing plant indicated the suitability of the proposed method in time series forecasting applications.

“…input by a user, gathered from a weather service, etc.). A detailed description of the modelling and prediction process was presented in [32], discussing the obtained accuracy results and the advantages of the algorithm's implementation over other similar methods.…”

Section: ) Demand Modelling and Predictionmentioning

confidence: 99%

Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms

Kampouropoulos

Andrade

et al. 2018

IEEE Trans. Smart Grid

Abstract-This paper presents a novel method for the energy optimization of multi-carrier energy systems. The presented method combines an adaptive neuro-fuzzy inference system, to model and forecast the power demand of a plant, and a genetic algorithm to optimize its energy flow taking into account the dynamics of the system and the equipment's thermal inertias. The objective of the optimization algorithm is to satisfy the total power demand of the plant and to minimize a set of optimization criteria, formulated as energy usage, monetary cost and environmental cost. The presented method has been validated under real conditions in the car manufacturing plant of SEAT in Spain in the framework of an FP7 European research project.