Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 8 Issue 9
Individual Household Electric Power Consumption Forecasting using Machine Learning Algorithms
Aaditi Parate, Sachin Bhoite
10.7753/IJCATR0809.1007
keywords : Energy consumption prediction, ARIMA, AR, MA, Python.
Electric energy consumption is the actual energy demand made on existing electricity supply. However, the mismanagement of its utilisation can lead to a fall in the supply of electricity. It is therefore imperative that everybody should be concerned about the efficient use of energy in order to reduce consumption [1]. The purposes of this research are to find a model to forecast the electricity consumption in a household and to find the most suitable forecasting period whether it should be in daily, weekly, monthly, or quarterly. The time series data in our study is the individual household electric power consumption [4].To explore and understand the dataset I used line plots for series data and histograms for the data distribution. The data analysis has been performed with the ARIMA (Autoregressive Integrated Moving Average) model.
@artical{a892019ijcatr08091007,
Title = "Individual Household Electric Power Consumption Forecasting using Machine Learning Algorithms",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "8",
Issue ="9",
Pages ="371 - 376",
Year = "2019",
Authors ="Aaditi Parate, Sachin Bhoite"}
We used ARIMA model for forecasting .
The model forecasts the week ahead using observations from the same time one year ago that achieved an overall RMSE of about 465 kilowatts.
The Data is multivariate time series and to understand the dataset we plotted different patterns in observations over time.
We used the first three years of data for training predictive models and the final year for evaluating models.