scholarly journals Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

2021 ◽  
Vol 11 (14) ◽  
pp. 6626
Author(s):  
Ayas Shaqour ◽  
Hooman Farzaneh ◽  
Huthaifa Almogdady
Keyword(s):  
Neural Networks ◽  
Price Elasticity ◽  
Demand Response ◽  
Deep Neural Networks ◽  
Response Model ◽  
Power Sector ◽  
Short Term ◽  
Peak Demand ◽  
Detailed Model ◽  
Residential Sector

In this paper, a comprehensive demand response model for the residential sector in the Jordanian electricity market is introduced, considering the interaction between the power generators (PGs), grid operators (GOs), and service providers (SPs). An accurate day-ahead hourly short-term load forecasting is conducted, using deep neural networks (DNNs) trained on four-year data collected from the National Electric Power Company (NEPCO) in Jordan. The customer behavior is modeled by developing a precise price elasticity matrix of demand (PEMD) based on recent research on the short-term price elasticity of Jordan’s residential and the analysis of the different types of electrical appliances and their daily operational hours according to the latest surveys. First, the DNNs are fine-tuned with a detailed feature analysis to predict the day-ahead hourly electrical demand and achieved a mean absolute percentage error (MAPE) of 1.365% and 1.411% on the validation and test datasets receptively. Then the predictions are used as input to a detailed model of the Jordanian power grid market, where a day-ahead peak-time demand response policy for the residential sector is applied to the three distribution power companies in Jordan. Based on different PEMD analyses for the Jordanian residential sector, the results suggest a reduction potential of 5.4% in peak demand accompanied by a cost reduction of USD 154,505 per day for the Jordanian power sector.

scholarly journals Short-term Traffic Prediction with Deep Neural Networks: A Survey

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Kyungeun Lee ◽  
Moonjung Eo ◽  
Euna Jung ◽  
Yoonjin Yoon ◽  
Wonjong Rhee
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Traffic Prediction ◽  
Short Term

Very short-term prediction of torrential rains using polarimetric phased-array radar (MP-PAWR) and deep neural networks

2021 ◽  
Author(s):  
Philippe Baron ◽  
Hiroshi Hanado ◽  
Dong-Kyun Kim ◽  
Seiji Kawamura ◽  
Takeshi Maesaka ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Phased Array ◽  
Short Term ◽  
Term Prediction ◽  
Phased Array Radar ◽  
Short Term Prediction

scholarly journals Forecasting Reservoir Water Levels Using Deep Neural Networks: A Case Study of Angat Dam in the Philippines

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 34
Author(s):  
Sebastian C. Ibañez ◽  
Carlo Vincienzo G. Dajac ◽  
Marissa P. Liponhay ◽  
Erika Fille T. Legara ◽  
Jon Michael H. Esteban ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
The Philippines ◽  
Water Levels ◽  
Short Term ◽  
Exogenous Variables ◽  
Reservoir Water ◽  
Forecasting Performance

Forecasting reservoir water levels is essential in water supply management, impacting both operations and intervention strategies. This paper examines the short-term and long-term forecasting performance of several statistical and machine learning-based methods for predicting the water levels of the Angat Dam in the Philippines. A total of six forecasting methods are compared: naïve/persistence; seasonal mean; autoregressive integrated moving average (ARIMA); gradient boosting machines (GBM); and two deep neural networks (DNN) using a long short-term memory-based (LSTM) encoder-decoder architecture: a univariate model (DNN-U) and a multivariate model (DNN-M). Daily historical water levels from 2001 to 2021 are used in predicting future water levels. In addition, we include meteorological data (rainfall and the Oceanic Niño Index) and irrigation data as exogenous variables. To evaluate the forecast accuracy of our methods, we use a time series cross-validation approach to establish a more robust estimate of the error statistics. Our results show that our DNN-U model has the best accuracy in the 1-day-ahead scenario with a mean absolute error (MAE) and root mean square error (RMSE) of 0.2 m. In the 30-day-, 90-day-, and 180-day-ahead scenarios, the DNN-M shows the best performance with MAE (RMSE) scores of 2.9 (3.3), 5.1 (6.0), and 6.7 (8.1) meters, respectively. Additionally, we demonstrate that further improvements in performance are possible by scanning over all possible combinations of the exogenous variables and only using a subset of them as features. In summary, we provide a comprehensive framework for evaluating water level forecasting by defining a baseline accuracy, analyzing performance across multiple prediction horizons, using time series cross-validation to assess accuracy and uncertainty, and examining the effects of exogenous variables on forecasting performance. In the process, our work addresses several notable gaps in the methodologies of previous works.

Modeling Long- and Short-Term User Behaviors for Sequential Recommendation with Deep Neural Networks

2021 ◽  
Author(s):  
Cairong Yan ◽  
Yiwei Wang ◽  
Yanting Zhang ◽  
Zijian Wang ◽  
Pengwei Wang
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Short Term ◽  
User Behaviors

scholarly journals Econometric Analysis of Residential Sector Gas Demand Elasticities in Gas Exporting Countries

2021 ◽  
Vol 11 (2) ◽  
pp. 1
Author(s):  
Eshagh Mansourkiaee ◽  
Hussein Moghaddam
Keyword(s):  
Price Elasticity ◽  
Income Elasticity ◽  
Bounds Testing ◽  
Short Term ◽  
Residential Sector ◽  
Price And Income Elasticities ◽  
Long Run ◽  
Short Run ◽  
Income And Price Elasticities ◽  
Price Policies

This paper examines how residential sector gas demand in gas exporting countries response to changes by taking into consideration the economic variables. For this purpose, the short and long-run price and income elasticities of residential sector gas demand in the GECF countries for 2000 and 2019 are measured. Using Cobb-Douglas functional form, this paper applies the bounds testing approach to co-integrate within the framework of ARDL (Autoregressive Distributed Lag). Findings of this research show that there is a significant long-run relationship in nine GECF countries, including Algeria, Egypt, Iran, Malaysia, Norway, Peru, Russia, Trinidad and Tobago and Venezuela, that use gas as a source of energy in their residential sector. On average, long-rung income elasticity for underlying countries is 2.65, while long-run price elasticity is negative and calculated at 0.79. This shows that in considered gas exporting countries, residential sector gas demand is very sensitive to income policies, while the price policies impact on demand is more limited. Furthermore, short-run income and price elasticities are estimated at 6.99 and -0.02 (near zero) respectively, which implies that natural gas is very inelastic to price, as a result,price policies are unable to make significant changes in demand over the short-term. Meanwhile, as expected short-run price elasticity is lower than long-run elasticities, indicating that gas exporting countries are more responsive to price in the long-term than in the short-term. Finally, it was found that most of the preferred models have empirical constancy over the sample period. 

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