Short Term Load Forecasting based on Deep Learning for Smart Grid Applications

Short-term load forecasting (STLF) is important for power system planning and optimization, especially in the dynamic environment of smart grid. Traditional load forecasting is implemented at substation levels to predict the upcoming active power and optimal system settings. In more advanced smart grid applications, e.g. the Volt-VAR Control, small-scale load forecasting opens up new opportunities in coordinating distributed resources such as distributed generation (DG) with utilities' efficiency missions. This paper proposes a STLF approach for small residential blocks with 10-12 households. The Nonlinear Autoregressive Neural Network (NAR-NN) is employed to predict hour-ahead active (P) and reactive (Q) powers with a moving window of training data. The regressor shrinkage technique, LASSO, is used to improve the selection of the regressors in the NAR-NN model by removing insignificant input features. The results show the forecasting performance could be enhanced by ~20% comparing to feed-forward Artificial Neural Networks (ANNs). The improvement in forecasting both P & Q could accommodate new smart grid applications in small scales.

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

IEEE Transactions on Smart Grid ◽

10.1109/tsg.2021.3091469 ◽

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

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

Applied Sciences ◽

10.3390/app11062742 ◽

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.

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An Efficient Regional Short-Term Load Forecasting Model for Smart Grid Energy Management

IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society ◽

10.1109/iecon43393.2020.9254736 ◽

2020 ◽

Author(s):

Ajit Muzumdar ◽

Chirag Modi ◽

C Vyjayanthi

Keyword(s):

Smart Grid ◽

Energy Management ◽

Load Forecasting ◽

Forecasting Model ◽

Short Term ◽

Short Term Load Forecasting

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Using deep learning for short-term load forecasting

Neural Computing and Applications ◽

10.1007/s00521-020-04856-0 ◽

2020 ◽

Vol 32 (18) ◽

pp. 15029-15041 ◽

Cited By ~ 1

Author(s):

Nadjib Mohamed Mehdi Bendaoud ◽

Nadir Farah

Keyword(s):

Deep Learning ◽

Load Forecasting ◽

Short Term ◽

Short Term Load Forecasting

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An ensemble deep learning model for short-term load forecasting based on ARIMA and LSTM

2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) ◽

10.1109/smartgridcomm.2019.8909756 ◽

2019 ◽

Cited By ~ 2

Author(s):

Lingling Tang ◽

Yulin Yi ◽

Yuexing Peng

Keyword(s):

Deep Learning ◽

Load Forecasting ◽

Learning Model ◽

Short Term ◽

Short Term Load Forecasting ◽

Deep Learning Model

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

Applied Sciences ◽

10.3390/app9091723 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1723 ◽

Cited By ~ 12

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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