Strategic management of energy consumption and reduction of specific energy consumption using modern methods of artificial intelligence in an industrial plant

Artificial Intelligence Based Methods for Asphaltenes Adsorption by Nanocomposites: Application of Group Method of Data Handling, Least Squares Support Vector Machine, and Artificial Neural Networks

(2020) Mohammad Sadegh Mazloom et al.   Nanomaterials

Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications

(2019) Ali Hassan Sodhro et al.   IEEE Transactions on Industrial Informatics

SVM and ANFIS for prediction of performance and exhaust emissions of a SI engine with gasoline–ethanol blended fuels

(2016) G. Najafi et al.   APPLIED THERMAL ENGINEERING

Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm

(2016) Eiman Tamah Al-Shammari et al.   ENERGY

Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm

(2016) Sasan Barak et al.   INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption

(2015) Nima Izadyar et al.   ENERGY

A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model

(2015) Jianzhou Wang et al.   ENERGY

Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon: Kinetic and isotherm study

(2013) M. Ghaedi et al.   JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY

A PSO and pattern search based memetic algorithm for SVMs parameters optimization

(2013) Yukun Bao et al.   NEUROCOMPUTING

Prediction of appliances energy use in smart homes

(2012) Nicoleta Arghira et al.   ENERGY

A review on the prediction of building energy consumption

(2012) Hai-xiang Zhao et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Performance comparison and parametric optimization of subcritical Organic Rankine Cycle (ORC) and transcritical power cycle system for low-temperature geothermal power generation

(2011) Zhang Shengjun et al.   APPLIED ENERGY

Study of working fluid selection of organic Rankine cycle (ORC) for engine waste heat recovery

(2011) E.H. Wang et al.   ENERGY

Nonlinear multivariable modeling of solid oxide fuel cells using core vector regression

(2011) M. Mohammadi et al.   INTERNATIONAL JOURNAL OF HYDROGEN ENERGY

Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data

(2010) Leonardo Ornella et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Supervised machine learning algorithms for protein structure classification

(2009) Pooja Jain et al.   COMPUTATIONAL BIOLOGY AND CHEMISTRY

Parametric optimization and comparative study of organic Rankine cycle (ORC) for low grade waste heat recovery

(2009) Yiping Dai et al.   ENERGY CONVERSION AND MANAGEMENT

Prediction of building energy consumption by using artificial neural networks

(2008) Betul Bektas Ekici et al.   ADVANCES IN ENGINEERING SOFTWARE

Evolutionary tuning of SVM parameter values in multiclass problems

(2008) Ana Carolina Lorena et al.   NEUROCOMPUTING

A review on buildings energy consumption information

(2007) Luis Pérez-Lombard et al.   ENERGY AND BUILDINGS

Modeling a ground-coupled heat pump system by a support vector machine

(2007) Hikmet Esen et al.   RENEWABLE ENERGY