scholarly journals Entropy-based member selection in a GCM ensemble forecasting

2006 ◽  
Vol 33 (2) ◽  
Author(s):  
F. J. Tapiador ◽  
C. Gallardo
Keyword(s):  
Ensemble Forecasting ◽  
Member Selection

The Simulation of Five Tropical Cyclones by Sample Optimization of Ensemble Forecasting Based on the Observed Track and Intensity

2021 ◽  
Author(s):  
Jihang Li ◽  
Zhiyan Zhang ◽  
Lu Liu ◽  
Xubin Zhang ◽  
Jingxuan Qu ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Ensemble Forecasting

scholarly journals C‐LAEF : Convection‐permitting Limited‐Area Ensemble Forecasting system

2021 ◽  
Author(s):  
Clemens Wastl ◽  
Yong Wang ◽  
Aitor Atencia ◽  
Florian Weidle ◽  
Christoph Wittmann ◽  
...  
Keyword(s):  
Ensemble Forecasting ◽  
Limited Area ◽  
Forecasting System

scholarly journals Evaluating the performance of two global ensemble forecasting systems in predicting rainfall over India during the southwest monsoons

2017 ◽  
Vol 24 (2) ◽  
pp. 230-238 ◽  
Author(s):  
Anumeha Dube ◽  
Raghavendra Ashrit ◽  
Harvir Singh ◽  
Kopal Arora ◽  
Gopal Iyengar ◽  
...  
Keyword(s):  
Ensemble Forecasting

scholarly journals Intermediate-term forecasting of aftershocks from an early aftershock sequence: Bayesian and ensemble forecasting approaches

2015 ◽  
Vol 120 (4) ◽  
pp. 2561-2578 ◽  
Author(s):  
Takahiro Omi ◽  
Yosihiko Ogata ◽  
Yoshito Hirata ◽  
Kazuyuki Aihara
Keyword(s):  
Aftershock Sequence ◽  
Ensemble Forecasting

scholarly journals An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3020
Author(s):  
Anam-Nawaz Khan ◽  
Naeem Iqbal ◽  
Atif Rizwan ◽  
Rashid Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Cluster Analysis ◽  
Deep Learning ◽  
Residential Buildings ◽  
Ensemble Forecasting ◽  
Forecasting Model ◽  
Ensemble Model ◽  
Learning Models ◽  
Short Term ◽  
Floor Level ◽  
Consumption Data

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.

Ocean ensemble forecasting. Part I: Ensemble Mediterranean winds from a Bayesian hierarchical model

2011 ◽  
Vol 137 (657) ◽  
pp. 858-878 ◽  
Author(s):  
Ralph F. Milliff ◽  
Alessandro Bonazzi ◽  
Christopher K. Wikle ◽  
Nadia Pinardi ◽  
L. Mark Berliner
Keyword(s):  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Ensemble Forecasting ◽  
Bayesian Hierarchical
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