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.

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 Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

Inventions ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 15
Author(s):  
Arash Moradzadeh ◽  
Hamed Moayyed ◽  
Sahar Zakeri ◽  
Behnam Mohammadi-Ivatloo ◽  
A. Pedro Aguiar
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Correlation Coefficient ◽  
Mean Squared Error ◽  
Load Forecasting ◽  
Short Term ◽  
Squared Error ◽  
Short Term Load Forecasting ◽  
Input Variables ◽  
Lstm Network

Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load 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 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 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

Day-ahead load forecasting using improved grey Verhulst model

2020 ◽  
Vol 18 (5) ◽  
pp. 1335-1348
Author(s):  
Ariel Mutegi Mbae ◽  
Nnamdi I. Nwulu
Keyword(s):  
Power Systems ◽  
Unit Commitment ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Short Term ◽  
Content Type ◽  
Dispatch System ◽  
System Maintenance ◽  
Electricity Load ◽  
Generator Unit

Purpose In the daily energy dispatch process in a power system, accurate short-term electricity load forecasting is a very important tool used by spot market players. It is a critical requirement for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The purpose of this study is to present an improved grey Verhulst electricity load forecasting model. Design/methodology/approach To test the effectiveness of the proposed model for short-term load forecast, studies made use of Kenya’s load demand data for the period from January 2014 to June 2019. Findings The convectional grey Verhulst forecasting model yielded a mean absolute percentage error of 7.82 per cent, whereas the improved model yielded much better results with an error of 2.96 per cent. Practical implications In the daily energy dispatch process in a power system, accurate short-term load forecasting is a very important tool used by spot market players. It is a critical ingredient for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The fact that the model uses actual Kenya’s utility data confirms its usefulness in the practical world for both economic planning and policy matters. Social implications In terms of generation and transmission investments, proper load forecasting will enable utilities to make economically viable decisions. It forms a critical cog of the strategic plans for power utilities and other market players to avoid a situation of heavy stranded investment that adversely impact the final electricity prices and the other extreme scenario of expensive power shortages. Originality/value This research combined the use of natural logarithm and the exponential weighted moving average to improve the forecast accuracy of the grey Verhulst forecasting model.

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

A load curve based fuzzy modeling technique for short-term load forecasting

2003 ◽  
Vol 135 (2) ◽  
pp. 279-303 ◽  
Author(s):  
S.E. Papadakis ◽  
J.B. Theocharis ◽  
A.G. Bakirtzis
Keyword(s):  
Load Forecasting ◽  
Fuzzy Modeling ◽  
Modeling Technique ◽  
Short Term ◽  
Load Curve ◽  
Short Term Load Forecasting

scholarly journals Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2692 ◽  
Author(s):  
Juncheng Zhu ◽  
Zhile Yang ◽  
Monjur Mourshed ◽  
Yuanjun Guo ◽  
Yimin Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Load Forecasting ◽  
Learning Approaches ◽  
Short Term ◽  
Learning Methods ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

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