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

Abstract

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.

Modeling Energy Demand—A Systematic Literature Review

“…Clustering Historic energy demand [21,48,49,81,82,[91][92][93]109,114,118,155,163,232,[234][235][236]263,268,279,322,323,326,328,342,353,355,360,369,372,395,403,428,476] Weather data [21,82,[91][92][93]114,118,163,263,275,348,353,476] Calendar data [48,92,93,109,…”

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

“…The method is applied for forecasting Korean residential and commercial building load data. Liu et al [143] also find that a sparse encoding network can improve the forecast for an LSTM at the household-level. Naeem et al [164] develop a day-ahead load forecast of an Australian network-grid using an Ensemble Empirical Mode Decomposition (EEMD) to decompose the signal into Intrinsic Mode Functions (IMF) and residuals.…”

Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment

“…Information security is discussed in [12], where risk propagation model based on the Susceptible-Exposed-Infected-Recovered (SEIR) infectious disease model is proposed for a smart grid. The high volatility and uncertainty of load profiles and the tremendous communication pressure are discussed in a two-stage household electricity demand estimation study by [13]. Investigation of voltage control at consumers connection points based on smart approach has recently been carried out by [14], proposing a voltage control system for use in the Russian distribution grid.…”