Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method

Han, Mengjie; Johari, Fatemeh; Huang, Pei; Zhang, Xingxing

doi:10.1016/j.enbenv.2022.02.011

Cited by 6 publications

(2 citation statements)

References 55 publications

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“…The models commonly used for learning and forecasting historical load and power price data are time series model: time series model is a statistical model based on time series data, which can be used to predict future trends and seasonal changes, but there are strict assumptions on data smoothness, autocorrelation., which need to meet certain preconditions, may not be flexible enough for nonlinear and complex relationships, prediction The disadvantages include limited accuracy. In the power system, the commonly used time series models include the ARIMA model (Zhang et al, 2022), seasonal ARIMA model Han et al (2022), exponential smoothing model (Zheng and Jin, 2022). ; regression model: regression model predicts future trends by establishing the relationship between load or power price and some related factors, but it may not be flexible enough for non-linear and complex relationships and has limited prediction accuracy.…”

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confidence: 99%

CNN-GRU model based on attention mechanism for large-scale energy storage optimization in smart grid

2023

Front. Energy Res.

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Introduction: Smart grid (SG) technologies have a wide range of applications to improve the reliability, economics, and sustainability of power systems. Optimizing large-scale energy storage technologies for smart grids is an important topic in smart grid optimization. By predicting the historical load and electricity price of the power system, a reasonable optimization scheme can be proposed.Methods: Based on this, this paper proposes a prediction model combining a convolutional neural network (CNN) and gated recurrent unit (GRU) based on an attention mechanism to explore the optimization scheme of large-scale energy storage in a smart grid. The CNN model can extract spatial features, and the GRU model can effectively solve the gradient explosion problem in long-term forecasting. Its structure is simpler and faster than LSTM models with similar prediction accuracy. After the CNN-GRU extracts the data, the features are finally weighted by the attention module to improve the prediction performance of the model further. Then, we also compared different forecasting models.Results and Discussion: The results show that our model has better predictive performance and computational power, making an important contribution to developing large-scale energy storage optimization schemes for smart grids.

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confidence: 99%

CNN-GRU model based on attention mechanism for large-scale energy storage optimization in smart grid

2023

Front. Energy Res.

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“…The current business models of the grids are more focused on energy production without consideration of future demands and having information about the customers who will be connected with grids due to the rapid construction of new buildings 3,4 . The advancements in smart homes have increased the burden on smart grids; hence energy consumption has also increased 5,6 . Current smart city facilities emphasize automation and security; companies are now focused on making smart homes, smart grids, and smart cities more energy-efficient.…”

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confidence: 99%

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Shaikh

Nazir

Khan

et al. 2022

Sci Rep

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Smart grids and smart homes are getting people’s attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with “day as covariates” remained better than the 1, 2, 3, and 4-week scenarios.

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Unsupervised anomaly detection of industrial building energy consumption

Song,

Kuang,

Huang

et al. 2024

Energy and Built Environment

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Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method

Cited by 6 publications

References 55 publications

CNN-GRU model based on attention mechanism for large-scale energy storage optimization in smart grid

CNN-GRU model based on attention mechanism for large-scale energy storage optimization in smart grid

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Unsupervised anomaly detection of industrial building energy consumption

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