Effective Electricity Demand Prediction via Deep Learning

2021 ◽  
Vol 10 (6) ◽  
pp. 483-489
Author(s):  
Daegun Ko ◽  
Youngmin Yoon ◽  
Jinoh Kim ◽  
Haelyong Choi
Keyword(s):  
Deep Learning ◽  
Electricity Demand ◽  
Demand Prediction

scholarly journals Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

2019 ◽  
Author(s):  
Amir Mosavi ◽  
Sina Faizollahzadeh Ardabili ◽  
Shahabodin Shamshirband
Keyword(s):  
Machine Learning ◽  
Energy Production ◽  
Production Management ◽  
Prediction Models ◽  
State Of The Art ◽  
Electricity Demand ◽  
Original Research ◽  
Review Of The Literature ◽  
Future Prospects ◽  
Demand Prediction

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

scholarly journals A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

2020 ◽  
Vol 12 (8) ◽  
pp. 3103 ◽  
Author(s):  
Hyojoo Son ◽  
Changwan Kim
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Power System ◽  
Electricity Demand ◽  
Efficient Production ◽  
Power System Planning ◽  
System Planning ◽  
Residential Sector ◽  
Use Of Resources ◽  
Proposed Model

Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.

scholarly journals Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3996 ◽  
Author(s):  
Marwen Elkamel ◽  
Lily Schleider ◽  
Eduardo L. Pasiliao ◽  
Ali Diabat ◽  
Qipeng P. Zheng
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Multiple Regression ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Multiple Regression Model ◽  
Electricity Demand ◽  
Degree Days ◽  
Population Number ◽  
Demand Prediction

Predicting future energy demand will allow for better planning and operation of electricity providers. Suppliers will have an idea of what they need to prepare for, thereby preventing over and under-production. This can save money and make the energy industry more efficient. We applied a multiple regression model and three Convolutional Neural Networks (CNNs) in order to predict Florida’s future electricity use. The multiple regression model was a time series model that included all the variables and employed a regression equation. The univariant CNN only accounts for the energy consumption variable. The multichannel network takes into account all the time series variables. The multihead network created a CNN model for each of the variables and then combined them through concatenation. For all of the models, the dataset was split up into training and testing data so the predictions could be compared to the actual values in order to avoid overfitting and to provide an unbiased estimate of model accuracy. Historical data from January 2010 to December 2017 were used. The results for the multiple regression model concluded that the variables month, Cooling Degree Days, Heating Degree Days and GDP were significant in predicting future electricity demand. Other multiple regression models were formulated that utilized other variables that were correlated to the variables in the best-selected model. These variables included: number of visitors to the state, population, number of consumers and number of households. For the CNNs, the univariant predictions had more diverse and higher Root Mean Squared Error (RMSE) values compared to the multichannel and multihead network. The multichannel network performed the best out of the three CNNs. In summary, the multichannel model was found to be the best at predicting future electricity demand out of all the models considered, including the regression model based on the datasets employed.

scholarly journals A Deep Learning Based Multi-Block Hybrid Model for Bike-Sharing Supply-Demand Prediction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 85826-85838 ◽  
Author(s):  
Miao Xu ◽  
Hongfei Liu ◽  
Hongbo Yang
Keyword(s):  
Deep Learning ◽  
Hybrid Model ◽  
Demand Prediction ◽  
Bike Sharing

scholarly journals Short-Term Prediction of Demand for Ride-Hailing Services: A Deep Learning Approach

2021 ◽  
Author(s):  
Long Chen ◽  
Piyushimita Vonu Thakuriah ◽  
Konstantinos Ampountolas
Keyword(s):  
New York ◽  
Deep Learning ◽  
Built Environment ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Service Providers ◽  
Road Traffic ◽  
Travel Behaviour ◽  
Demand Prediction ◽  
Research Areas

AbstractAs ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve traffic congestion, and enhance the passenger experience. This paper proposes UberNet, a deep learning convolutional neural network for short-time prediction of demand for ride-hailing services. Exploiting traditional time series approaches for this problem is challenging due to strong surges and declines in pickups, as well as spatial concentrations of demand. This leads to pickup patterns that are unevenly distributed over time and space. UberNet employs a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. Specifically, the proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To assess the performance and effectiveness of UberNet, we use 9 months of Uber pickup data in 2014 and 28 spatial and temporal features from New York City. We use a number of features suggested by the transport operations and travel behaviour research areas as being relevant to passenger demand prediction, e.g., weather, temporal factors, socioeconomic and demographics characteristics, as well as travel-to-work, built environment and social factors such as crime level, within a multivariate framework, that leads to operational and policy insights for multiple communities: the ride-hailing operator, passengers, third-part location-based service providers and revenue opportunities to drivers, and transport operators such as road traffic authorities, and public transport agencies. By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive. Further, Ubernet’s prediction performance is better when using economic, social and built environment features. This suggests that Ubernet is more naturally suited to including complex motivators of travel behavior in making real-time demand predictions for ride-hailing services.

Sign in / Sign up

Export Citation Format

Share Document