Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting

Alhussein, Musaed; Aurangzeb, Khursheed; Haider, Syed Irtaza

doi:10.1109/access.2020.3028281

Cited by 305 publications

(170 citation statements)

References 42 publications

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“…The CNN network structure regards the input as an image, and by compiling specific features into the convolution structure, the efficiency of the forward transfer function is improved and the number of parameters in the network is reduced [18]- [19]. The one-dimensional convolutional layer can realize the feature extraction of the time axis, and its output is:…”

Section: ⅲ Deep Learning Model Principle a Cnn Modelmentioning

confidence: 99%

Multi-Model Fusion Short-Term Load Forecasting Based on Random Forest Feature Selection and Hybrid Neural Network

Zhu

et al. 2021

IEEE Access

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In an increasingly open electricity market environment, short-term load forecasting (STLF) can ensure the power grid to operate safely and stably, reduce resource waste, power dispatching, and provide technical support for demand-side response. Recently, with the rapid development of demand side response, accurate load forecasting can better provide demand side incentive for regional load of prosumer groups. Traditional machine learning prediction and time series prediction based on statistics failed to consider the non-linear relationship between various input features, resulting in the inability to accurately predict load changes. Recently, with the rapid development of deep learning, extensive research has been carried out in the field of load forecasting. On this basis, a feature selection algorithm based on random forest is first used in this paper to provide a basis for the selection of the input features of the load forecasting model. After the input features are selected, a hybrid neural network STLF algorithm based on multi-model fusion is proposed, of which the main structure of the hybrid neural network is composed of convolutional neural network and bidirectional gated recurrent unit (CNN-BiGRU). The input data is obtained by using long sliding time windows of different steps, then multiple CNN-BiGRU models are trained respectively. The forecasting results of multiple models are averaged to get the final forecasting load value. The load datasets come from a region in New Zealand and a region in Zhejiang, China, are used as load forecast examples. Finally, a variety of load forecasting algorithms are introduced for comparison. The experimental results show that our method has a higher accuracy than comparison models.

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Section: ⅲ Deep Learning Model Principle a Cnn Modelmentioning

confidence: 99%

Multi-Model Fusion Short-Term Load Forecasting Based on Random Forest Feature Selection and Hybrid Neural Network

Zhu

et al. 2021

IEEE Access

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“…For neural networks and SVM, they obtained MAPE of 51 percent and 48 percent, respectively. Several other researchers proposed and evaluated several different effective models for prediction of electric loads [43]- [47]. Specifically, in [47], we proposed a Hybrid deep learning model which, is composed of convolutional layers and LSTM layers, where the focus has been on power load forecasting of individual energy customer.…”

Section: Literature Reviewmentioning

confidence: 99%

A Pyramid-CNN Based Deep Learning Model for Power Load Forecasting of Similar-Profile Energy Customers Based on Clustering

et al. 2021

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With rapid advancements in renewable energy sources, billing mechanism (AMI), and latest communication technologies, the traditional control networks are evolving towards wise, versatile and collaborative Smart Grids (SG). The short term power load forecasting of individual as well as group of similar energy customers is critical for effective operation and management of SG. Forecasting power load of individual as well as group of similar energy customers is challenging compared to aggregate load forecasting of a residential community. The main reason is the high volatility and uncertainty involved for the case of individual and group of similar energy customers. Several machine/deep learning models have been developed in the recent past for forecasting load of individual energy customers, but such explorations are ineffective due to the requirement of one trained model for every energy customer, which is practically not feasible. We plan to build a deep learning model using convolutional neural network (CNN) layers in pyramidal architecture for effective load forecasting for a group of similar energy-profile customers. Initially, we grouped a subset of energy customers from database of Smart Grid Smart City (SGSC) into clusters using DBSCAN approach. The CNN layers are used for extracting feature from historical load of each cluster. The extracted feature of similar energy-profile customers (grouped based on clustering) is combined to make training-databases for each cluster. We have used the power load data from SGSC project, which contain thousands of individual household energy customers data. The developed Pyramid-CNN model is trained based on these sets of databases. The trained model is evaluated on randomly selected customers from few clusters. We obtained significantly improved forecasting results for randomly selected user from different clusters. Our adapted strategy of clustering based model training resulted in upto 10 percent MAPE improvement for the energy customers. The essence of our work is that energy customers can be grouped into clusters and then representative model could be developed/trained, which can accurately forecast power load for individual energy-customer. This approach is highly feasible, as we do not need to train a model per energy customer and still achieve competitive forecasting results.

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“…The authors in [31], [32] considered the CNN-based approach for load forecasting of individual households and reported better performance results in comparison to other DL methods. Intelligent forecasting techniques based on hybrid CNN-LSTM methods convincingly outperform baseline CNN or LSTM architectures across a wide range of forecasting tasks [33].…”

Section: Related Workmentioning

confidence: 99%

Multi-Horizon Electricity Load and Price Forecasting Using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework

Azam

Younis

2021

IEEE Access

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Accurate system marginal price and load forecasts play a pivotal role in economic power dispatch, system reliability and planning. Price forecasting helps electricity buyers and sellers in an energy market to make effective decisions when preparing their bids and making bilateral contracts. Despite considerable research work in this domain, load and price forecasting still remains to be a complicated task. Various uncertain elements contribute to electricity price and demand volatility, such as changes in weather conditions, outages of large power plants, fuel cost, complex bidding strategies and transmission congestion in the power system. Thus, to deal with such difficulties, we propose a novel hybrid deep learning method based upon bidirectional long short-term memory (BiLSTM) and a multi-head self-attention mechanisms that can accurately forecast locational marginal price (LMP) and system load on a day-ahead basis. Additionally, ensemble empirical mode decomposition (EEMD), a data-driven algorithm, is used for the extraction of hidden features from the load and price time series. Besides that, an intuitive understanding of how the proposed model works under the hood is demonstrated using different interpretability use cases. The performance of the presented method is compared with existing well-known techniques applied for short-term electricity load and price forecast in a comprehensive manner. The proposed method produces considerably accurate results in comparison to existing benchmarks.INDEX TERMS Deep learning, energy markets, ensemble empirical mode decomposition, multi-head self-attention, short-term load and price forecasting, kalman smoothing, model interpretability.

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Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting

Cited by 305 publications

References 42 publications

Multi-Model Fusion Short-Term Load Forecasting Based on Random Forest Feature Selection and Hybrid Neural Network

Multi-Model Fusion Short-Term Load Forecasting Based on Random Forest Feature Selection and Hybrid Neural Network

A Pyramid-CNN Based Deep Learning Model for Power Load Forecasting of Similar-Profile Energy Customers Based on Clustering

Multi-Horizon Electricity Load and Price Forecasting Using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework

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