Multi-Timescale Load Forecast of Large Power Customers Based on Online Data Recovery and Time Series Neural Networks

This paper presents a multi-timescale receding horizon framework for the load forecast of large power customers. The future load pattern of individual users could be very difficult to predict because of its chronological and high volatile properties. Also, the sampling of nonaggregated load data may suffer from severe information missing issues. To address these challenges, we first develop an online singular value thresholding (SVT) algorithm, which utilizes the approximate low-rank property of load data matrices to efficiently recover the missing information. Then, a combinatorial deep learning method is developed, which applies the multi-layer perception (MLP) neural network and the long short-term memory (LSTM) neural network with gated recurrent unit (GRU) to deal with the short-term and ultra-short-term load forecast, respectively. Specifically, an early stopping strategy is designed and implemented to avoid the over-fitting of model training. Moreover, the receding time window is imposed to dynamically update the data recovery and load forecast outcomes, which supports the online computing on a Spark platform. Numerical experiments on real-world load data from North China confirms the effectiveness of the proposed methodology, which can support more complex applications in embedded systems and cyber physical systems.

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A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network

Energies ◽

10.3390/en11123493 ◽

2018 ◽

Vol 11 (12) ◽

pp. 3493 ◽

Cited By ~ 60

Author(s):

Chujie Tian ◽

Jian Ma ◽

Chunhong Zhang ◽

Panpan Zhan

Keyword(s):

Neural Network ◽

Real World ◽

Deep Neural Network ◽

Short Term Memory ◽

Electrical Load ◽

Short Term ◽

Term Memory ◽

Load Forecast ◽

Proposed Model ◽

Long Short Term Memory

Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.

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Time Window from Visual Images to Visual Short-Term Memory: Consolidation or Integration?

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169.51.1.45 ◽

2004 ◽

Vol 51 (1) ◽

pp. 45-51 ◽

Cited By ~ 21

Author(s):

Yuhong Jiang

Keyword(s):

Short Term Memory ◽

Time Window ◽

Short Interval ◽

Visual Images ◽

Short Term ◽

Temporal Window ◽

Term Memory ◽

Visual Short Term Memory ◽

Probe Task ◽

Long Time

Abstract. When two dot arrays are briefly presented, separated by a short interval of time, visual short-term memory of the first array is disrupted if the interval between arrays is shorter than 1300-1500 ms ( Brockmole, Wang, & Irwin, 2002 ). Here we investigated whether such a time window was triggered by the necessity to integrate arrays. Using a probe task we removed the need for integration but retained the requirement to represent the images. We found that a long time window was needed for performance to reach asymptote even when integration across images was not required. Furthermore, such window was lengthened if subjects had to remember the locations of the second array, but not if they only conducted a visual search among it. We suggest that a temporal window is required for consolidation of the first array, which is vulnerable to disruption by subsequent images that also need to be memorized.

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Estimation of municipal solid waste amount based on one-dimension convolutional neural network and long short-term memory with attention mechanism model: A case study of Shanghai

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.148088 ◽

2021 ◽

Vol 791 ◽

pp. 148088

Author(s):

Kunsen Lin ◽

Youcai Zhao ◽

Lu Tian ◽

Chunlong Zhao ◽

Meilan Zhang ◽

...

Keyword(s):

Neural Network ◽

Municipal Solid Waste ◽

Convolutional Neural Network ◽

Short Term Memory ◽

One Dimension ◽

Short Term ◽

Term Memory ◽

Mechanism Model ◽

Long Short Term Memory

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Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

Sustainability ◽

10.3390/su13010104 ◽

2020 ◽

Vol 13 (1) ◽

pp. 104

Author(s):

Dana-Mihaela Petroșanu ◽

Alexandru Pîrjan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Short Term Memory ◽

Electricity Consumption ◽

Short Term ◽

Term Memory ◽

Storage Room ◽

Long Short Term Memory ◽

Commercial Center ◽

Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

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