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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Prediction of Energy Consumption Using Statistical and Machine Learning Methods and Analyzing the Significance of Climate and Holidays in the Demand Prediction
verfasst von : Naveen Tata, Srivasthasva Srinivas Machiraju, V. Akshay, Divyasree Mohan Menon, N. B. Sai Shibu, D. Arjun
Erschienen in: Advances in Computing and Network Communications
Verlag: Springer Singapore
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Abstract
With the increase in the development of smart metering in energy systems, a large amount of data is being generated. The data consists of energy generated, energy consumed and energy stored with respect to time. This data can be used to improve the efficiency, reliability and stability of the power system by using machine learning algorithms. Energy requirement of each consumer can be predicted with the available data. Renewable energy generation can also be predicted. In this paper, different statistical and machine learning models are used to analyze the energy usage in smart communities. To validate the prediction models, smart meter data from our campus is used. The results show that the long short-term memory (LSTM) model is more suitable for energy demand prediction. The LSTM model is then used to predict the energy demand in students’ hostels during conditions such as climate and holidays.