A Clustering-Based Fuzzy Wavelet Neural Network Model for Short-Term Load Forecasting

Kodogiannis, Vassilis; Amina, Mahdi; Petrounias, Ilias

doi:10.1142/s012906571350024x

Cited by 70 publications

(43 citation statements)

References 30 publications

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“…An energy modeling tool, eQuest, is utilized and the development discussion of the LCA residential model is explained to study the use phase. Shi and Xie (2009) propose an environmental problems and resources consumption evaluation model for green construction by combining the fuzzy set theory (Yan, Ma 2012;Liu, Er 2012;Fougères, Ostrosi 2013;Kodogiannis et al 2013) and a quality function deployment method. They use a value engineering approach to solving the problem of the green construction alternatives optimization.…”

Section: Life Cycle Cost Assessmentmentioning

confidence: 99%

Sustainable Building Design

Wang

Adeli

2014

Journal of Civil Engineering and Management

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Abstract. Sustainable building design has become a wide and multidisciplinary research endeavor including mechanical, electrical, electronic, communication, acoustic, architectural, and structural engineering. It involves the participation of owners, contractors, suppliers and building users. There has been a lot of talk about sustainable buildings in the past few years. Most of the published research is concerned with saving energy and water and making the buildings more environmentally friendly by, say, reducing the carbon emissions. In this article, sustainable building design is reviewed from the viewpoint of structural engineering. Different strategies presented in the literature are summarized. Finally, the authors argue that the next big leap in sustainable building design should come from the integration of the smart structure technology including the use of hybrid and semi-active vibration controllers that can result in substantially lighter and more efficient structures.

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Section: Life Cycle Cost Assessmentmentioning

confidence: 99%

Sustainable Building Design

Wang

Adeli

2014

Journal of Civil Engineering and Management

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“…The primary criterion used for the classification of forecasting models is the forecasting horizon. Mocanu et al [1] grouped electricity demand forecasting into three categories, short-term forecasts ranging between one hour and one load data [27], the fuzzy neural [28], wavelet neural networks [29], fuzzy wavelet neural network [30] and self-originating map (SOM), neural network [31], were used mainly for STLF.…”

Section: Introductionmentioning

confidence: 99%

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

et al. 2018

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Abstract:Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as this requires training several different models for selecting the best amongst them along with substantial feature engineering to derive informative features and finding optimal time lags, a commonly used input features for time series models. Methods: Our approach uses machine learning and a long short-term memory (LSTM)-based neural network with various configurations to construct forecasting models for short to medium term aggregate load forecasting. The research solves above mentioned problems by training several linear and non-linear machine learning algorithms and picking the best as baseline, choosing best features using wrapper and embedded feature selection methods and finally using genetic algorithm (GA) to find optimal time lags and number of layers for LSTM model predictive performance optimization. Results: Using France metropolitan's electricity consumption data as a case study, obtained results show that LSTM based model has shown high accuracy then machine learning model that is optimized with hyperparameter tuning. Using the best features, optimal lags, layers and training various LSTM configurations further improved forecasting accuracy. Conclusions: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area.

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“…Wavelets are known to have good modelling properties over a range of frequencies; hence they have been utilised in neuro-fuzzy (NF) systems (Kodogiannis, et al, 2013). Generally, the fuzzy wavelet neural network (FWNN) is a combined structure based on fuzzy rules that includes wavelet functions in their consequent parts, in the form of a wavelet neural network.…”

Section: Cfwnn Architecturementioning

confidence: 99%

Application of an Electronic Nose Coupled with Fuzzy-Wavelet Network for the Detection of Meat Spoilage

Kodogiannis

2017

Food Bioprocess Technol

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Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage Kodogiannis, V.This is an author's accepted manuscript of an article published in Food and Bioprocess Technology, doi: 10.1007/s11947-016-1851-6 in 2017.The final publication is available at Springer via:https://dx.doi.org/10.1007/s11947-016-1851-6The WestminsterResearch online digital archive at the University of Westminster aims to make the research output of the University available to a wider audience. Copyright and Moral Rights remain with the authors and/or copyright owners.Whilst further distribution of specific materials from within this archive is forbidden, you may freely distribute the URL of WestminsterResearch: ((http://westminsterresearch.wmin.ac.uk/). Abstract:Food product safety is one of the most promising areas for the application of electronic noses. During the last twenty years, these sensor-based systems have made odour analyses possible. Their application into the area of food is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network model has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population. Comparison results against advanced machine learning schemes indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology.

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A Clustering-Based Fuzzy Wavelet Neural Network Model for Short-Term Load Forecasting

Cited by 70 publications

References 30 publications

Sustainable Building Design

Sustainable Building Design

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

Application of an Electronic Nose Coupled with Fuzzy-Wavelet Network for the Detection of Meat Spoilage

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