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Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid
- Source :
- Inventions, Volume 6, Issue 1, Inventions, Vol 6, Iss 15, p 15 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- 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.
- Subjects :
- lcsh:HD45-45.2
Mean squared error
Correlation coefficient
lcsh:Engineering machinery, tools, and implements
energy management
Computer science
Energy management
020209 energy
020208 electrical & electronic engineering
General Engineering
residential and commercial loads
deep learning
short-term load forecasting
02 engineering and technology
Reliability engineering
Term (time)
bidirectional long short-term memory (Bi-LSTM)
Electric power system
microgrid
0202 electrical engineering, electronic engineering, information engineering
Microgrid
lcsh:TA213-215
lcsh:Technological innovations. Automation
Demand load
Energy (signal processing)
Subjects
Details
- Language :
- English
- ISSN :
- 24115134
- Database :
- OpenAIRE
- Journal :
- Inventions
- Accession number :
- edsair.doi.dedup.....aba50ba753e4f129668bc3358f8c1acc
- Full Text :
- https://doi.org/10.3390/inventions6010015