Energy consumption

Engineering

Dew point

Statistics

Time series

Operating system

Computer science

Cloud computing

Regression analysis

Visibility

Mathematics

Geography

Cloud cover

Autoregressive integrated moving average

Environmental science

Multicollinearity

Meteorology

Machine learning

Electrical engineering

Sustainable Energy Technologies and Assessments

Energy consumption forecasting is essential for smart grid operations as it facilitates electricity demand management and utilities load planning. In this paper data analytics has been presented on the collected smart meter measurement and then predicting the energy consumption on a daily basis using (autoregressive integrated moving average) ARIMA, seasonal ARIMA (SARIMA) and long short-term memory (LSTM). The analysis tends to understand the different factors which influence energy consumption, and assist operators to make decisions accordingly. It is helpful in reducing the outage, and enhancing the situational awareness of power consumption on a daily basis of the smart meters. The relational factors are capable in lowering energy consumption, or rather contributing to the effective consumption of energy units. The parameters used for the result evaluation are various features of the weather features relation in terms of power consumption based on temperature, humidity, cloud cover, visibility, wind speed, UV index and dew point. The results indicate that the energy consumption has a high positive correlation with humidity and high negative correlation with temperature. (Dew point and UV index) and (Cloud cover and Visibility Display) have multicollinearity with temperature and humidity respectively, so, can be discarded. Pressure and Moon Phase have minimal correlation with energy consumption, so, it can also be discarded. Wind speed has low correlation with energy, but it does not show multicollinearity. So, it can be considered for further analysis. Overall LSTM found to be prominent in comparison to ARIMA and SARIMA with the average mean absolute error (MAE) of 0.23.

Study and analysis of SARIMA and LSTM in forecasting time series data