Abstract
Power grids are transforming into flexible, smart, and cooperative systems with greater dissemination of distributed energy resources, advanced metering infrastructure, and advanced communication technologies. Short-term electric load forecasting for individual residential customers plays a progressively crucial role in the operation and planning of future grids. Compared to the aggregated electrical load at the community level, the prediction of individual household electric loads is legitimately challenging because of the high uncertainty and volatility involved. Results from previous studies show that prediction using machine learning and deep learning models is far from accurate, and there is still room for improvement. We herein propose a deep learning framework based on a combination of a convolutional neural network (CNN) and long short-term memory (LSTM). The proposed hybrid CNN-LSTM model uses CNN layers for feature extraction from the input data with LSTM layers for sequence learning. The performance of our developed framework is comprehensively compared to state-of-the-art systems currently in use for short-term individual household electric load forecasting. The proposed model achieved significantly better results compared to other competing techniques. We evaluated our proposed model with the recently explored LSTM-based deep learning model on a publicly available electrical load data of individual household customers from the Smart Grid Smart City (SGSC) project. We obtained an average mean absolute percentage error (MAPE) of 40.38% for individual household electric load forecasts in comparison with the LSTM-based model that obtained an average MAPE of 44.06%. Furthermore, we evaluated the effectiveness of the proposed model on different time horizons (up to 3 h ahead). Compared to the recently developed LSTM-based model tested on the same dataset, we obtained 4.01%, 4.76%, and 5.98% improvement for one, two, and six look-forward time steps, respectively (with 2 lookback time steps). Additionally, we have performed clustering analysis based on the power consumption behavior of the energy users, which indicate that prediction accuracy could be improved by grouping and training the representative model using large amount of data. The results indicated that the proposed model outperforms the LSTM-based model for both 1 h ahead and 3 h ahead in forecasting individual household electric loads.
Highlights
Short-term electric load forecasting is a vital part of the energy sector since it concerns the forecasting of power consumption in the subsequent few hours
The results show that the exploitation of convolutional layers along with long short-term memory (LSTM) layers could provide a significant improvement in the accuracy of individual household load forecasting
Our proposed model, which is based on a combination of convolutional neural network (CNN) and LSTM quite closely follow the actual original load at almost all the time instances
Summary
Short-term electric load forecasting is a vital part of the energy sector since it concerns the forecasting of power consumption in the subsequent few hours. The major contributions of this paper are: (1) developing a hybrid CNN-LSTM model, which can exploit the benefits of convolutional layers and LSTM layers; (2) Illustrating the effectiveness of the proposed model in individual household load forecasting in comparison with existing state-of-the-art methods; (3) validating the efficacy of the proposed model for various time horizons; (4) investigating the clustering behavior by grouping customers with structural similarity in their load profiles. Their proposed model outperformed all the rival techniques and achieved an average MAPE of 44.06% for short-term residential load forecasting of 69 customers Based on these recent explorations conducted by valuable researchers, there is a vibrant and increasingly understandable research tendency that looks at challenges related to behavioral and other factors that have an impact on the energy consumption of the individual household. A hybrid model that exploits the benefits of both CNN and LSTM is expected to enhance the load forecasting accuracy of the individual household
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