Hourly Electricity Load Forecasting in Smart Grid Using Deep Learning Techniques

2019 ◽  
pp. 185-196
Author(s):  
Abdul Basit Majeed Khan ◽  
Nadeem Javaid ◽  
Orooj Nazeer ◽  
Maheen Zahid ◽  
Mariam Akbar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Smart Grid ◽  
Load Forecasting ◽  
Learning Techniques ◽  
Electricity Load ◽  
Electricity Load Forecasting

scholarly journals Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models

2021 ◽  
Author(s):  
Muhammed SÜTÇÜ ◽  
İbrahim Tümay GÜLBAHAR ◽  
Kübra Nur ŞAHİN ◽  
Yunus KOLOĞLU ◽  
Mevlüt Emirhan ÇELİKEL
Keyword(s):  
Deep Learning ◽  
Load Forecasting ◽  
Hybrid Models ◽  
Electricity Load ◽  
Electricity Load Forecasting

scholarly journals Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning

Designs ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 27
Author(s):  
Navid Shirzadi ◽  
Ameer Nizami ◽  
Mohammadali Khazen ◽  
Mazdak Nik-Bakht
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Smart Grids ◽  
Load Forecasting ◽  
Percentage Error ◽  
Support Vector ◽  
Medium Term ◽  
Load Demand ◽  
Electricity Load ◽  
Electricity Load Forecasting

Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has become a crucial need. Such forecasts are necessary to support the plans and decisions related to the capacity evaluation of centralized and decentralized power generation systems, demand response strategies, and controlling the operation. To address this problem, the main objective of this study is to develop and compare precise district level models for predicting the electrical load demand based on machine learning techniques including support vector machine (SVM) and Random Forest (RF), and deep learning methods such as non-linear auto-regressive exogenous (NARX) neural network and recurrent neural networks (Long Short-Term Memory—LSTM). A dataset including nine years of historical load demand for Bruce County, Ontario, Canada, fused with the climatic information (temperature and wind speed) are used to train the models after completing the preprocessing and cleaning stages. The results show that by employing deep learning, the model could predict the load demand more accurately than SVM and RF, with an R-Squared of about 0.93–0.96 and Mean Absolute Percentage Error (MAPE) of about 4–10%. The model can be used not only by the municipalities as well as utility companies and power distributors in the management and expansion of electricity grids; but also by the households to make decisions on the adoption of home- and district-scale renewable energy technologies.

Electricity load forecasting in a smart grid system

2016 ◽  
Vol 20 (5) ◽  
pp. 1223-1242 ◽  
Author(s):  
Chia-Yu Shen ◽  
Hsiao-Fan Wang
Keyword(s):  
Smart Grid ◽  
Load Forecasting ◽  
Grid System ◽  
Electricity Load ◽  
Smart Grid System ◽  
Electricity Load Forecasting
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