Electricity demand estimation using proxy variables: some reservations

1983 ◽  
Vol 15 (5) ◽  
pp. 703-704 ◽  
Author(s):  
T. P. Roth
Keyword(s):  
Demand Estimation ◽  
Electricity Demand ◽  
Proxy Variables

An efficient edge sparse coding approach to ultra-short-term household electricity demand estimation

2018 ◽  
Vol 13 (11) ◽  
pp. 1586-1594
Author(s):  
Yi Sun ◽  
Yaoxian Liu ◽  
Lu Zhang ◽  
Yongfeng Cao ◽  
Xiongwen Zhao
Keyword(s):  
Sparse Coding ◽  
Demand Estimation ◽  
Electricity Demand ◽  
Short Term

Regional Electricity Demand Estimation to Realize Society5.0

2020 ◽  
Vol 2020 (0) ◽  
pp. F01107
Author(s):  
Hideki FUJII ◽  
Tomofumi TAHARA ◽  
Hideaki UCHIDA ◽  
Shinobu YOSHIMURA
Keyword(s):  
Demand Estimation ◽  
Electricity Demand

scholarly journals A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 224 ◽  
Author(s):  
Yaoxian Liu ◽  
Yi Sun ◽  
Bin Li
Keyword(s):  
Sparse Coding ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
Absolute Error ◽  
Demand Estimation ◽  
Electricity Demand ◽  
Accurate Estimation ◽  
Smart Meters ◽  
Two Stage

The widespread popularity of smart meters enables the collection of an immense amount of fine-grained data, thereby realizing a two-way information flow between the grid and the customer, along with personalized interaction services, such as precise demand response. These services basically rely on the accurate estimation of electricity demand, and the key challenge lies in the high volatility and uncertainty of load profiles and the tremendous communication pressure on the data link or computing center. This study proposed a novel two-stage approach for estimating household electricity demand based on edge deep sparse coding. In the first sparse coding stage, the status of electrical devices was introduced into the deep non-negative k-means-singular value decomposition (K-SVD) sparse algorithm to estimate the behavior of customers. The patterns extracted in the first stage were used to train the long short-term memory (LSTM) network and forecast household electricity demand in the subsequent 30 min. The developed method was implemented on the Python platform and tested on AMPds dataset. The proposed method outperformed the multi-layer perception (MLP) by 51.26%, the autoregressive integrated moving average model (ARIMA) by 36.62%, and LSTM with shallow K-SVD by 16.4% in terms of mean absolute percent error (MAPE). In the field of mean absolute error and root mean squared error, the improvement was 53.95% and 36.73% compared with MLP, 28.47% and 23.36% compared with ARIMA, 11.38% and 18.16% compared with LSTM with shallow K-SVD. The results of the experiments demonstrated that the proposed method can provide considerable and stable improvement in household electricity demand estimation.

scholarly journals Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of China

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Qunli Wu ◽  
Chenyang Peng
Keyword(s):  
Quadratic Form ◽  
Quadratic Forms ◽  
Bat Algorithm ◽  
Demand Estimation ◽  
Electricity Demand ◽  
Carbon Intensity ◽  
Path Coefficient ◽  
Global Optimality ◽  
Path Coefficient Analysis ◽  
Estimation Model

Path-coefficient analysis is utilized to investigate the direct and indirect effects of economic growth, population growth, urbanization rate, industrialization level, and carbon intensity on electricity demand of China. To improve the projection accuracy of electricity demand, this study proposes a hybrid bat algorithm, Gaussian perturbations, and simulated annealing (BAG-SA) optimization method. The proposed BAG-SA algorithm not only inherits the simplicity and efficiency of the standard BA with a capability of searching for global optimality but also enhances local search ability and speeds up the global convergence rate. The BAG-SA algorithm is employed to optimize the coefficients of the multiple linear and quadratic forms of electricity demand estimation model. Results indicate that the proposed algorithm has higher precision and reliability than the coefficients optimized by other single-optimization methods, such as genetic algorithm, particle swarm optimization algorithm, or bat algorithm. And the quadratic form of BAG-SA electricity demand estimation model has better fitting ability compared with the multiple linear form of the model. Therefore, the quadratic form of the model is applied to estimate electricity demand of China from 2016 to 2030. The findings of this study demonstrate that China’s electricity demand will reach 14925200 million KWh in 2030.

Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada

Energy ◽  
2013 ◽  
Vol 49 ◽  
pp. 323-328 ◽  
Author(s):  
Gholamreza Zahedi ◽  
Saeed Azizi ◽  
Alireza Bahadori ◽  
Ali Elkamel ◽  
Sharifah R. Wan Alwi
Keyword(s):  
Demand Estimation ◽  
Electricity Demand ◽  
Neuro Fuzzy ◽  
Fuzzy Network

A Study about Estimation of Aggregated Electricity Demand Based on a Classification of Electricity Demand and Household's Attributes

2016 ◽  
Vol 136 (6) ◽  
pp. 802-810 ◽  
Author(s):  
Kei Morita ◽  
Yuichi Nakano ◽  
Osamu Sadakane ◽  
Yusuke Manabe ◽  
Takeyoshi Kato ◽  
...  
Keyword(s):  
Electricity Demand
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