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

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.

COVID-19 and the Electricity Market

2022 ◽  
pp. 124-144
Author(s):  
Nima Norouzi
Keyword(s):  
Electricity Market ◽  
Electricity Consumption ◽  
Electricity Industry ◽  
Electricity Demand ◽  
The State ◽  
Short Term ◽  
The World ◽  
Relative Decline

This chapter investigates the effects of COVID-19 on electricity consumption in some countries, especially in Iran. The effect of COVID-19 in the electricity industry and the amount of electricity consumption in Iran and in the countries that have been most affected have been studied. A study of COVID-19's impact on the world shows a reduction of about 15% in electricity demand during the short term of the COVID-19 outbreak. This amount varies from country to country. Studies show that the countries under study have experienced a relative decline in electricity demand in the short term, but with the continued prevalence of COVID-19 and the removal of some restrictions, the state of electricity consumption has more or less returned to pre-COVID-19 levels. It is worth noting that at the time of writing this chapter, the COVID-19 pandemic continues.

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