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The Application of Deep Learning Models in Estimating the Energy of Residential Buildings
Mohammadzadeh, Mohammad | 2020
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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 53257 (01)
- University: Sharif University of Technology
- Department: Industrial Engineering
- Advisor(s): Rafiee, Majid; Shavandi, Hassan
- Abstract:
- Electricity consumption has increased dramatically in recent decades, and this increase has severely affected electricity distribution. Therefore, forecasting electricity demand can provide a precondition for distributors. Predicting power consumption requires many parameters to be considered.In this research, machine learning, and deep learning methods such as recursive neural networks, long short-term memory networks, etc., as well as the ARIMA model will be used. These models have been tested on the London Smart Measurement Database. In order to evaluate the capability of the models in forecasting electricity consumption, each has been used to predict the electricity consumption of a specific house and a set of houses in a certain period of time. Forecasts are designed for daily, quarterly, and thirteen-month intervals to cover short-term, medium-term, and long-term forecasts
- Keywords:
- Electricity Consumption Efficiency ; Deep Learning ; Time Series ; Recursive Artificial Neural Networks ; Residential Buildings ; Demand Forecasting ; Energy Demand
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