scholarly journals Prediction of energy consumption using recurrent neural networks (RNN) and nonlinear autoregressive neural network with external input (NARX)

2020 ◽  
Vol 17 (3) ◽  
pp. 1215
Author(s):  
Wan Muhammad Zafri Wan Yahaya ◽  
Fadhlan Hafizhelmi Kamaru Zaman ◽  
Mohd Fuad Abdul Latip
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Consumption ◽  
Recurrent Neural Networks ◽  
Electrical Energy ◽  
External Input ◽  
Smart Meters ◽  
Depth Analysis ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

Recurrent Neural Networks (RNN) and Nonlinear Autoregressive Neural Network with External Input (NARX) are recently applied in predicting energy consumption. Energy consumption prediction for depth analysis of how electrical energy consumption is managed on Tower 2 Engineering Building is critical in order to reduce the energy usage and the operational cost. Prediction of energy consumption in this building will bring great benefits to the Faculty of Electrical Engineering UiTM Shah Alam. In this work, we present the comparative study on the performance of prediction of energy consumption in Tower 2 Engineering Building using RNN and NARX method. The model of RNN and NARX are trained using data collected using smart meters installed inside the building. The results after training and testing using RNN and NARX show that by using the recorded data we can accurately predict the energy consumption in the building. We also show that RNN model trained with normalized data performs better than NARX model.

scholarly journals Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

Buildings ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 204 ◽  
Author(s):  
Yang ◽  
Tan ◽  
Santamouris ◽  
Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Consumption ◽  
Missing Data ◽  
Recurrent Neural Networks ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Data Set ◽  
Raw Data ◽  
Deep Recurrent Neural Network

With the rising focus on building energy big data analysis, there lacks a framework for raw data preprocessing to answer the question of how to handle the missing data in the raw data set. This study presents a methodology and framework for building energy consumption raw data forecasting. A case building is used to forecast the energy consumption by using deep recurrent neural networks. Four different methodologies to impute missing data in the raw data set are compared and implemented. The question of sensitivity of gap size and available data percentage on the imputation accuracy was tested. The cleaned data were then used for building energy forecasting. While the existing studies explored only the use of small recurrent networks of 2 layers and less, the question of whether a deep network of more than 2 layers would be performing better for building energy consumption forecasting should be explored. In addition, the problem of overfitting has been cited as a significant problem in using deep networks. In this study, the deep recurrent neural network is then used to explore the use of deeper networks and their regularization in the context of an energy load forecasting task. The results show a mean absolute error of 2.1 can be achieved through the 2*32 gated neural network model. In applying regularization methods to overcome model overfitting, the study found that weights regularization did indeed delay the onset of overfitting.

scholarly journals Wind speed forecasting using neural networks

2019 ◽  
Vol 44 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Tyler Blanchard ◽  
Biswanath Samanta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
The United States ◽  
Weather Data ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

The prediction of wind speed is critical in the assessment of feasibility of a potential wind turbine site. This work presents a study on prediction of wind speed using artificial neural networks. Two variations of artificial neural networks, namely, nonlinear autoregressive neural network and nonlinear autoregressive neural network with exogenous inputs, were used to predict wind speed utilizing 1 year of hourly weather data from four locations around the United States to train, validate, and test these networks. This study optimized both neural network configurations and it demonstrated that both models were suitable for wind speed prediction. Both models outperformed persistence model (with a factor of about 2 to 10 in root mean square error ratio). Both artificial neural network models were implemented for single-step and multi-step-ahead prediction of wind speed for all four locations and results were compared. Nonlinear autoregressive neural network with exogenous inputs model gave better prediction performance than nonlinear autoregressive model and the difference was statistically significant.

scholarly journals Modeling of Harmonic Current in Electrical Grids with Photovoltaic Power Integration Using a Nonlinear Autoregressive with External Input Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4015
Author(s):  
Adán Alberto Jumilla-Corral ◽  
Héctor Enrique Campbell-Ramírez ◽  
Carlos Perez-Tello ◽  
Zulma Yadira Medrano-Hurtado ◽  
Pedro Mayorga-Ortiz ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
External Input ◽  
Electric Power Supply ◽  
Photovoltaic Power ◽  
Electrical Grids ◽  
The Neural Network ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

This research presents the modeling and prediction of the harmonic behavior of current in an electric power supply grid with the integration of photovoltaic power by inverters using artificial neural networks to determine if the use of the proposed neural network is capable of capturing the harmonic behavior of the photovoltaic energy integrated into the user's electrical grids. The methodology used was based on the use of recurrent artificial neural networks of the nonlinear autoregressive with external input type. Work data were obtained from experimental sources through the use of a test bench, measurement, acquisition, and monitoring equipment. The input–output parameters for the neural network were the current values in the inverter and the supply grid, respectively. The results showed that the neural network can capture the dynamics of the analyzed system. The generated model presented flexibility in data handling, allowing to represent and predict the behavior of the harmonic phenomenon. The obtained algorithm can be transferred to physical or virtual systems for the control or reduction of harmonic distortion.

Modeling the Levelized Cost of Energy for Concentrating Solar Thermal Power Systems Based on a Nonlinear AutoRegressive Neural Network With Exogenous Inputs

2021 ◽  
Vol 10 (4) ◽  
pp. 1-17
Author(s):  
Natalya Filippchenkova
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Power Systems ◽  
Carbon Dioxide Emissions ◽  
Thermal Power ◽  
Levelized Cost ◽  
Cost Of Energy ◽  
Domestic Energy ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

This article presents the results of the development of a mathematical model for predicting the levelized cost of energy (LCOE) for solar concentrating thermal power systems (CSP systems) based on a nonlinear autoregressive neural network with exogenous inputs (NARX). A two-layer NARX network with sigmoid hidden neurons and linear output neurons has been developed. The input layer is made up of the following variables: the volume of input power of CSP systems in the world, the total world energy consumption, domestic energy consumption, domestic gas consumption, domestic consumption of coal and lignite, domestic energy consumption, the share of renewable energy in electricity generation, the share of wind and solar energy in the production of electricity, carbon dioxide emissions from fuel combustion, the price of Brent oil against the US dollar, and the average price for natural gas auctions. The output layer specifies LCOE values for CSP systems.

SOH estimation method for lithium-ion batteries based on nonlinear autoregressive neural network model with exogenous input

2021 ◽  
pp. 1-28
Author(s):  
Yanbo Che ◽  
Yibin Cai ◽  
Hongfeng Li ◽  
Yushu Liu ◽  
Mingda Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
Variable Order ◽  
Nonlinear Correlation ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

Abstract The working state of lithium-ion batteries must be estimated accurately and efficiently in the battery management system. Building a model is the most prevalent way of predicting the battery's working state. Based on the variable order equivalent circuit model, this paper examines the attenuation curve of battery capacity with the number of cycles. It identifies the order of the equivalent circuit model using Bayesian Information Criterion (BIC). Based on the correlation between capacity and resistance, the paper concludes that there is a nonlinear correlation between model parameters and state of health (SOH). The nonlinear autoregressive neural network with exogenous input (NARX) is used to fit the nonlinear correlation for capacity regeneration. Then, the self-adaptive weight particle swarm optimization (SWPSO) method is suggested to train the neural network. Finally, single-battery and multi-battery tests are planned to validate the accuracy of the SWPSO-NARX estimate of SOH. The experimental findings indicate that the SOH estimate effect is significant.

scholarly journals Recurrent Neural Networks for Narrowband Signal Detection in the Time-Frequency Domain

2004 ◽  
Vol 213 ◽  
pp. 483-486
Author(s):  
David Brodrick ◽  
Douglas Taylor ◽  
Joachim Diederich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Signal Detection ◽  
Frequency Domain ◽  
Recurrent Neural Networks ◽  
Radio Frequency Interference ◽  
Recurrent Networks ◽  
Time Frequency ◽  
Narrowband Signal ◽  
Radio Signals

A recurrent neural network was trained to detect the time-frequency domain signature of narrowband radio signals against a background of astronomical noise. The objective was to investigate the use of recurrent networks for signal detection in the Search for Extra-Terrestrial Intelligence, though the problem is closely analogous to the detection of some classes of Radio Frequency Interference in radio astronomy.

scholarly journals The interaction between structure and meaning in sentence comprehension: Recurrent neural networks and reading times

2019 ◽  
Author(s):  
Stefan L. Frank ◽  
John Hoeks
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sentence Processing ◽  
Recurrent Neural Networks ◽  
Sentence Comprehension ◽  
Semantic Relations ◽  
Garden Path ◽  
Word Predictability ◽  
Syntactic Ambiguities ◽  
Potential Object

Recurrent neural network (RNN) models of sentence processing have recently displayed a remarkable ability to learn aspects of structure comprehension, as evidenced by their ability to account for reading times on sentences with local syntactic ambiguities (i.e., garden-path effects). Here, we investigate if these models can also simulate the effect of semantic appropriateness of the ambiguity's readings. RNNs-based estimates of surprisal of the disambiguating verb of sentences with an NP/S-coordination ambiguity (as in `The wizard guards the king and the princess protects ...') show identical patters to human reading times on the same sentences: Surprisal is higher on ambiguous structures than on their disambiguated counterparts and this effect is weaker, but not absent, in cases of poor thematic fit between the verb and its potential object (`The teacher baked the cake and the baker made ...'). These results show that an RNN is able to simultaneously learn about structural and semantic relations between words and suggest that garden-path phenomena may be more closely related to word predictability than traditionally assumed.

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