Open AccessAnalysis of energy consumption using RNN-LSTM and ARIMA Model
Author(s) -
Mahindrakar Manisha Sachin,
Melvin Paily Baby,
Abraham Sudharson Ponraj
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1716/1/012048
Subject(s) - electricity , autoregressive integrated moving average , consumption (sociology) , computer science , recurrent neural network , time series , energy consumption , econometrics , stand alone power system , mains electricity , electricity generation , smart grid , multivariate statistics , environmental economics , power (physics) , artificial neural network , artificial intelligence , economics , machine learning , engineering , electrical engineering , social science , physics , quantum mechanics , sociology
Given the increase of smart electricity meters and the wide adoption of electricity generation technologies such as solar panels, there is a wealth of data available on the usage of electricity. This data represents a multivariate time series of variables related to power, which could in turn be used to model and even forecast future electricity usage. The Household Power Consumption dataset is a multivariate time series dataset which describes the electricity consumption over four years for a single household. They were tested to predict for a specific house and block of houses over a given period of time. Throughout the past couple of decades energy demand has increased exponentially. This increase loads the electricity distributors heavily. So forecasting future demand for electricity use would give the dealer an upper hand. Predicting the consumption of energy requires several parameters. This paper proposes two methods with one using a Recurrent Neural Network (RNN) and another using a Long Short Term Memory (LSTM) network, considering only the previous consumption of electricity to estimate potential consumption of electricity. To assess the applicability of the RNN and the LSTM network to predict the electricity consumption.