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

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.

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

Sensors ◽

10.3390/s21051639 ◽

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.

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

Energies ◽

10.3390/en14217378 ◽

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.

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

Applied Sciences ◽

10.3390/app11178129 ◽

2021 ◽

Vol 11 (17) ◽

pp. 8129 ◽

Cited By ~ 1

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.

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