scholarly journals Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7378
Author(s):  
Pedro M. R. Bento ◽  
Jose A. N. Pombo ◽  
Maria R. A. Calado ◽  
Silvio J. P. S. Mariano
Keyword(s):  
Deep Learning ◽  
New England ◽  
Network Architecture ◽  
Prediction Models ◽  
Load Forecasting ◽  
Training Data ◽  
Constant Field ◽  
Short Term ◽  
Wide Range ◽  
Short Term Load Forecasting

Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and its participants need to better inform their decisions. Hence, it is important to continue advancing in terms of forecasting accuracy and consistency. This paper presents a new deep learning-based ensemble methodology for 24 h ahead load forecasting, where an automatic framework is proposed to select the best Box-Jenkins models (ARIMA Forecasters), from a wide-range of combinations. The method is distinct in its parameters but more importantly in considering different batches of historical (training) data, thus benefiting from prediction models focused on recent and longer load trends. Afterwards, these accurate predictions, mainly the linear components of the load time-series, are fed to the ensemble Deep Forward Neural Network. This flexible type of network architecture not only functions as a combiner but also receives additional historical and auxiliary data to further its generalization capabilities. Numerical testing using New England market data validated the proposed ensemble approach with diverse base forecasters, achieving promising results in comparison with other state-of-the-art methods.

scholarly journals A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning

2021 ◽  
Vol 11 (6) ◽  
pp. 2742
Author(s):  
Fatih Ünal ◽  
Abdulaziz Almalaq ◽  
Sami Ekici
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Unified Framework ◽  
Short Term ◽  
Planning And Scheduling ◽  
Short Term Load Forecasting

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

scholarly journals Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8129 ◽  
Author(s):  
Changchun Cai ◽  
Yuan Tao ◽  
Tianqi Zhu ◽  
Zhixiang Deng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Power Systems ◽  
Network Architecture ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Short Term ◽  
Architecture Model ◽  
Power Load ◽  
Short Term Load Forecasting

Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity of power loads in power systems. Hence, accurate and robust load forecasting results are becoming increasingly important in modern power systems. The paper presents a multi-layer stacked bidirectional long short-term memory (LSTM)-based short-term load forecasting framework; the method includes neural network architecture, model training, and bootstrapping. In the proposed method, reverse computing is combined with forward computing, and a feedback calculation mechanism is designed to solve the coupling of before and after time-series information of the power load. In order to improve the convergence of the algorithm, deep learning training is introduced to mine the correlation between historical loads, and the multi-layer stacked style of the network is established to manage the power load information. Finally, actual data are applied to test the proposed method, and a comparison of the results of the proposed method with different methods shows that the proposed method can extract dynamic features from the data as well as make accurate predictions, and the availability of the proposed method is verified with real operational data.

scholarly journals Electrical Load Prediction for Short Term using Support Vector Machine Techniques

2019 ◽  
Vol 9 (1S3) ◽  
pp. 111-116
Keyword(s):  
Support Vector Machine ◽  
Prediction Models ◽  
Load Forecasting ◽  
Training Data ◽  
Support Vector ◽  
Electrical Load ◽  
Load Prediction ◽  
Short Term ◽  
Training Time ◽  
Short Term Load Forecasting

The electrical load prediction during an interval of a week or a day plays an important role for scheduling and controlling operations of any power system. The techniques which are presently being used and are used for Short Term Load Forecasting (STLF) by utilizing various prediction models try for the performance improvement. The prediction models and their performance mainly depend upon the training data and its quality. The different forecasting approaches using Support Vector Machine (SVM) depending on several performance indices has been discussed. The accuracy of the forecasting approaches is measured by Mean Absolute Error (MAE), Root Mean Square Error (RMSE), prediction speed and training time. The approach with least RMSE reveals as the best among the SVM methods for short term load forecasting.

scholarly journals An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1639
Author(s):  
Seungmin Jung ◽  
Jihoon Moon ◽  
Sungwoo Park ◽  
Eenjun Hwang
Keyword(s):  
Power Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Input Sequence ◽  
Short Term ◽  
Performance Improvements ◽  
Short Term Load Forecasting ◽  
Significant Performance ◽  
Input Variables

Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

Hybrid multitask multi-information fusion deep learning for household short-term load forecasting

2021 ◽  
pp. 1-1
Author(s):  
Lianjie Jiang ◽  
Xinli Wang ◽  
Wei Li ◽  
Lei Wang ◽  
Xiaohong Yin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Information Fusion ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 54992-55008
Author(s):  
Dabeeruddin Syed ◽  
Haitham Abu-Rub ◽  
Ali Ghrayeb ◽  
Shady S. Refaat ◽  
Mahdi Houchati ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Smart Grid ◽  
Load Forecasting ◽  
Consumption Pattern ◽  
Short Term ◽  
Short Term Load Forecasting

Using deep learning for short-term load forecasting

2020 ◽  
Vol 32 (18) ◽  
pp. 15029-15041 ◽  
Author(s):  
Nadjib Mohamed Mehdi Bendaoud ◽  
Nadir Farah
Keyword(s):  
Deep Learning ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches

2019 ◽  
Vol 9 (9) ◽  
pp. 1723 ◽  
Author(s):  
Juncheng Zhu ◽  
Zhile Yang ◽  
Yuanjun Guo ◽  
Jiankang Zhang ◽  
Huikun Yang
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Unit Commitment ◽  
Combustion Engine ◽  
Load Forecasting ◽  
Learning Approaches ◽  
Short Term ◽  
Load Curve ◽  
Short Term Load Forecasting ◽  
Gated Recurrent Units

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.

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