scholarly journals Transportation Energy Demand Modeling with Artificial Neural Networks

2021 ◽  
pp. 2706-2715
Author(s):  
Merve Kayacı ÇODUR
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Demand ◽  
Transportation Energy ◽  
Demand Modeling ◽  
Artificial Neural

scholarly journals A Methodology for Energy Load Profile Forecasting Based on Intelligent Clustering and Smoothing Techniques

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4040
Author(s):  
Jamer Jiménez Mares ◽  
Loraine Navarro ◽  
Christian G. Quintero M. ◽  
Mauricio Pardo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Demand ◽  
Percentage Error ◽  
Artificial Neural ◽  
Short Term Forecasting ◽  
Dynamic Time ◽  
High Level ◽  
Energy Assets ◽  
Demand Changes

The electrical sector needs to study how energy demand changes to plan the maintenance and purchase of energy assets properly. Prediction studies for energy demand require a high level of reliability since a deviation in the forecasting demand could affect operation costs. This paper proposed a short-term forecasting energy demand methodology based on hierarchical clustering using Dynamic Time Warp as a similarity measure integrated with Artificial Neural Networks. Clustering was used to build the typical curve for each type of day, while Artificial Neural Networks handled the weather sensibility to correct a preliminary forecasting curve obtained in the clustering stage. A statistical analysis was carried out to identify those significant factors in the prediction model of energy demand. The performance of this proposed model was measured through the Mean Absolute Percentage Error (MAPE). The experimental results show that the three-stage methodology was able to improve the MAPE, reaching values as good as 2%.

Deploying artificial neural networks for modeling energy demand: international evidence

2019 ◽  
Vol 14 (2) ◽  
pp. 285-315 ◽  
Author(s):  
Emmanuel Bannor B. ◽  
Alex O. Acheampong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Demand ◽  
Trade Openness ◽  
Back Propagation ◽  
Rank Correlation ◽  
Back Propagation Algorithm ◽  
Content Type ◽  
The Usa ◽  
Artificial Neural

Purpose This paper aims to use artificial neural networks to develop models for forecasting energy demand for Australia, China, France, India and the USA. Design/methodology/approach The study used quarterly data that span over the period of 1980Q1-2015Q4 to develop and validate the models. Eight input parameters were used for modeling the demand for energy. Hyperparameter optimization was performed to determine the ideal parameters for configuring each country’s model. To ensure stable forecasts, a repeated evaluation approach was used. After several iterations, the optimal models for each country were selected based on predefined criteria. A multi-layer perceptron with a back-propagation algorithm was used for building each model. Findings The results suggest that the validated models have developed high generalizing capabilities with insignificant forecasting deviations. The model for Australia, China, France, India and the USA attained high coefficients of determination of 0.981, 0.9837, 0.9425, 0.9137 and 0.9756, respectively. The results from the partial rank correlation coefficient further reveal that economic growth has the highest sensitivity weight on energy demand in Australia, France and the USA while industrialization has the highest sensitivity weight on energy demand in China. Trade openness has the highest sensitivity weight on energy demand in India. Originality/value This study incorporates other variables such as financial development, foreign direct investment, trade openness, industrialization and urbanization, which are found to have an important effect on energy demand in the model to prevent underestimation of the actual energy demand. Sensitivity analysis is conducted to determine the most influential variables. The study further deploys the models for hands-on predictions of energy demand.

scholarly journals An Estimation of Transport Energy Demand in Turkey via Artificial Neural Networks

2019 ◽  
Vol 31 (2) ◽  
pp. 151-161 ◽  
Author(s):  
Muhammed Yasin Çodur ◽  
Ahmet Ünal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Demand ◽  
Motor Vehicle ◽  
Quantitative Estimation ◽  
Transportation Systems ◽  
Model Parameters ◽  
Primary Concern ◽  
Transportation Sector ◽  
Artificial Neural

The transportation sector accounts for nearly 19% of total energy consumption in Turkey, where energy demand increases rapidly depending on the economic and human population growth and the increasing number of motor vehicles. Hence, the estimation of future energy demand is of great importance to design, plan and use the transportation systems more efficiently, for which a reliable quantitative estimation is of primary concern. However, the estimation of transport energy demand is a complex task, since various model parameters are interacting with each other. In this study, artificial neural networks were used to estimate the energy demand in transportation sector in Turkey. Gross domestic product, oil prices, population, vehicle-km, ton-km and passenger-km were selected as parameters by considering the data for the period from 1975 to 2016. Seven models in total were created and analyzed. The best yielding model with the parameters of oil price, population and motor vehicle-km was determined to have the lowest error and the highest R2 values. This model was selected to estimate transport energy demand for the years 2020, 2023, 2025 and 2030.

scholarly journals Artificial Neural Networks to Optimize Zero Energy Building (ZEB) Projects from the Early Design Stages

2021 ◽  
Vol 11 (12) ◽  
pp. 5377
Author(s):  
Marco Pittarello ◽  
Massimiliano Scarpa ◽  
Aurora Greta Ruggeri ◽  
Laura Gabrielli ◽  
Luigi Schibuola
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Energy Demand ◽  
Building Energy ◽  
Assessment Tools ◽  
Machine Learning Techniques ◽  
Zero Energy ◽  
Zero Energy Building ◽  
Artificial Neural

Building energy modeling (BEM) is used to support (nearly) zero-energy building (ZEB) projects, since this kind of software represents the only available option to forecast building energy consumption with high accuracy. BEM may also be used during preliminary analyses or feasibility studies, but simulation results are usually too detailed for this stage of the project. Aside from that, when optimization algorithms are used, the implied high number of energy simulations causes very long calculation times. Therefore, designers could be discouraged from the extensive use of BEM to conduct optimization analyses. Thus, they prefer to study and compare a very limited amount of acknowledged alternative designs. In relation to this problem, the scope of the present study is to obtain an easy-to-use tool to quickly forecast the energy consumption of a building with no direct use of BEM to support fast comparative analyses at the early stages of energy projects. In response, a set of automatic energy assessment tools was developed based on machine learning techniques. The forecasting tools are artificial neural networks (ANNs) that are able to estimate the energy consumption automatically for any building, based on a limited amount of descriptive data of the property. The ANNs are developed for the Po Valley area in Italy as a pilot case study. The ANNs may be very useful to assess the energy demand for even a considerable number of buildings by comparing different design options, and they may help optimization analyses.

scholarly journals Management of battery charging and discharging in a photovoltaic system with variable power demand using artificial neural networks

2021 ◽  
Vol 297 ◽  
pp. 01037
Author(s):  
Jarmouni Ezzitouni ◽  
Mouhsen Ahmed ◽  
Lamhammedi Mohammed ◽  
Krari Ayoub
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Demand ◽  
Fossil Fuels ◽  
Management Strategies ◽  
Photovoltaic System ◽  
Proposed Model ◽  
Artificial Neural ◽  
Control And Management ◽  
Solar Batteries

The energy is the basis of all human activities. Nowadays, much of the world’s energy demand is taken from fossil fuels. However, fossil fuel reserves are limited. The use of solar photovoltaic energy is therefore a necessity for the future. With the rapid increase of photovoltaic or hybrid systems, solar batteries provide an unforgettable energy storage tool in this type of systems in order to ensure an energy supply to consumers. Due to the sensitivity of solar batteries and the random operation of photovoltaic systems that depend on solar irradiance, control and management strategies are quite important. In this paper, we present a technique based on artificial neural networks to control the charging and discharging of solar batteries in order to protect the batteries from overcharging and deep discharging. In addition, ensuring continuous supply to consumers. The proposed model is developed and simulated in Matlab/Simulink.

Sign in / Sign up

Export Citation Format

Share Document