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Analysis of a Residential Building Energy Consumption Demand Model

Wei Yu, Baizhan Li, Yarong Lei, Meng Liu
2011 Energies  
The index system of the BP neutral network prediction model is established and the multi-factorial BP neural network prediction model of Chongqing residential building energy consumption is developed using  ...  Then it proposes and develops a residential building energy consumption demand model based on a back propagation (BP) neural network model. After that, taking residential buildings in Chongqing (P.R.  ...  Acknowledgements This paper is funded by the project (50838009) of the National Natural Science Foundation of China, and by the project (CDJXS1021002) of Chongqing University Postgraduates' Science and  ... 
doi:10.3390/en4030475 fatcat:qoryogpanbaghhpcy5brdqg3d4

Predicting Heat Demand for a District Heating Systems

Krzysztof Wojdyga
2014 International Journal of Energy and Power Engineering  
The artificial neural networks model delivers good forecasting results.  ...  Described forecasting methods can be use as a good tool to regulate district heating networks and heat sources.  ...  Forecasting with the Use of Neural Networks Artificial neural networks (ANN) may provide a means of forecasting values in time series.  ... 
doi:10.11648/j.ijepe.20140305.13 fatcat:xqvekzcogrhedeaiqrgiaxxory

Efficient Energy Planning with Decomposition-Based Evolutionary Neural Networks

Tanveer Ahmad, Wahab Ali Shah, Dongdong Zhang
2020 IEEE Access  
This study proposes six decomposition-based evolutionary neural networks for city-scale and building energy forecasting.  ...  INDEX TERMS Evolutionary neural networks, energy forecasting, energy security, selective combination prediction, energy planning models.  ...  The authors gratefully acknowledged the support of UM Macao Postdoctoral Fellowship of University of Macau.  ... 
doi:10.1109/access.2020.3010782 fatcat:ajari3w7lrghxdelceftacxg2y

Spatial granularity analysis on electricity consumption prediction using LSTM recurrent neural network

Zhuang Zheng, Hainan Chen, Xiaowei Luo
2019 Energy Procedia  
The main scope of this paper is to assess the feasibility of using the heat demand -outdoor temperature function for heat demand forecast.  ...  District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector.  ...  The conclusions herein are those of the authors and do not necessarily reflect the views of the sponsoring agency.  ... 
doi:10.1016/j.egypro.2019.02.027 fatcat:qfi6lcah4jcajhvcei44io35fi

A Predictive Framework for Electricity Consumption

Patrick Ozoh, Shapiee Abd-Rahman, Jane Labadin
2016 Journal of IT in Asia  
In order to meet energy demand through the use of electricity as an energy source for daily activities in buildings such as air conditioning, lighting, computers and cooking stoves., adequate allocation  ...  The performances of regression model, artificial neural network and Kalman algorithm were tested using statistical evaluation parameters, root mean squared error (RMSE) and mean absolute percentage error  ...  Previous methods of using artificial neural network technique to forecast energy models were affected by approximations necessary for estimating data.  ... 
doi:10.33736/jita.331.2016 fatcat:ssthuegevjevlf5oyu5ysht26a

Energy Household Forecast with ANN for Demand Response and Demand Side Management

Filipe Rodrigues, Carlos Cardeira, J.M.F. Calado, R. Melício
2016 The Renewable Energies and Power Quality Journal (RE&PQJ)  
In the short term load forecasting model, artificial neural networks were used, with a consumption records database.  ...  This paper presents a short term load forecasting with artificial neural networks.  ...  Forecast hourly and daily energy consumption can be useful on the demand-side management, such as electricity suppliers, to forecast the likely future development of electricity demand in the entire sector  ... 
doi:10.24084/repqj14.559 fatcat:6xb5r3ola5cxdmyxbn6ncv2gde

Prediction of Future Electric Energy Consumption using Machine Learning Framework

2020 International Journal of Engineering and Advanced Technology  
In this system we have a projector that builds proper state for a particular condition and using that defined state a future power demand is forecasted by the predictor.  ...  In this paper we propose a model for predicting future demand for energy according to different conditions using advanced machine learning framework.  ...  In last few years, development of electricity demand forecasting models using machine learning have been of great interest.  ... 
doi:10.35940/ijeat.c5829.029320 fatcat:nif6opboy5bjbcjyasvj5y7cga

An Overview: the Development of Prediction Technology of Wind and Photovoltaic Power Generation

Z.W. Zheng, Y.Y. Chen, M.M. Huo, B. Zhao
2011 Energy Procedia  
The energy management information system has became a research hotspot with the rapid development of smart grid, which using for the integration of micro-grid and traditional electric power grid.  ...  filtering, data mining and wavelet transform, etc.) and artificial intelligence (such as neural networks, fuzzy inference and biological intelligence algorithm, etc.).  ...  Acknowledgements This work was supported by Natural Science Foundation of Zhejiang Province (Y1111254) and Zhejiang nonprofit applied research projects (2010C31021).  ... 
doi:10.1016/j.egypro.2011.10.081 fatcat:7bwvrbusazdx7d3nvj2ua2m7ae

A new grey forecasting model based on BP neural network and Markov chain

Cun-bin Li, Ke-cheng Wang
2007 Journal of Central South University of Technology  
A new grey forecasting model based on BP neural network and Markov chain was proposed.  ...  Based on the above discussion, three different approaches were put forward for forecasting China electricity demands.  ...  Introduction Energy demand forecasting is very important to formulate energy policy of country, especially for those countries whose energy demand is growing quickly.  ... 
doi:10.1007/s11771-007-0136-7 fatcat:atcirr7gvfbmhi6jdwznhk7vem

State of the art of machine learning models in energy systems, a systematic review

Mohsen Salimi, Amir Mosavi, Sina Faizollahzadeh Ardabili, Majid Amidpour, Timon Rabczuk, Shahabodin Shamshirband
2020 Zenodo  
Machine learning (ML) models have been widely used in diverse applications of energy systems such as design, modeling, complex mappings, system identification, performance prediction, and load forecasting  ...  This paper presents the state of the art of ML models used in energy systems along with a taxonomy of applications and methods.  ...  (2016) [35] studied control of hybrid renewable energy systems using recurrent neural networks to forecast weather conditions.  ... 
doi:10.5281/zenodo.4056884 fatcat:yf6gjoevffc2xnzoyjlc3pxbii

The Daily and Hourly Energy Consumption and Load Forecasting Using Artificial Neural Network Method: A Case Study Using a Set of 93 Households in Portugal

Filipe Rodrigues, Carlos Cardeira, J.M.F. Calado
2014 Energy Procedia  
In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the  ...  Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household  ...  Acknowledgements This work was partially supported by FCT, through IDMEC, under LAETA Pest-OE/EME/LA0022 and by the Sustainable Urban Energy System (SUES) project under the MIT Portugal Program (MPP).  ... 
doi:10.1016/j.egypro.2014.12.383 fatcat:ysfehh5ubfbhxptia7necbwwpm

Various Electricity Load Forecasting Techniques with Pros and Cons

2020 International journal of recent technology and engineering  
This paper presents a vast, rigorous and comparable survey of tremendous data mining techniques useful in forecasting the electricity load demand of different geographic area.  ...  The rapid growth of stored information in the demand forecasting, associated with data analysis provoked an utmost need for generating a powerful tool which must be capable of extracting hidden and vital  ...  Neural Networks: There are lots of methods and techniques which are very useful for demand forecasting.  ... 
doi:10.35940/ijrte.f6997.038620 fatcat:nhpc7ewxqrbajg7dqe4ebvm6ui

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

Jason Runge, Radu Zmeureanu
2019 Energies  
This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing  ...  Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/en12173254 fatcat:zbqk5s3igjdadko6h5pyiqkcwa

Application Of Neural Network For Forecasting Gas Price In America

Mehdi Sotoudeh, Elahe Farshad
2012 Journal of Mathematics and Computer Science  
This paper presents a neuro-base approach for gas price forecasting of American consumers. in order to forming a neural network structure, effecting parameters on gas price are analyzed and gas production  ...  used to train the multi-level ANN and the rest from 2005 to 2010 are used for network test.  ...  The application of artificial neural networks for stock market and energy forecasting problems have resulted in several research papers. B.  ... 
doi:10.22436/jmcs.04.02.11 fatcat:u7skoc4rzbgmjipnc4nmzwwszy

Machine Learning on EPEX Order Books: Insights and Forecasts [article]

Simon Schnürch, Andreas Wagner
2019 arXiv   pre-print
Using cross-validation to optimise hyperparameters, neural networks and random forests are proposed and compared to statistical reference models.  ...  The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected demand.  ...  We also use the random forest to support feature selection for the following neural network approach.  ... 
arXiv:1906.06248v3 fatcat:4lpg7x5dxjf5nl6mh76ed4xvx4
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