TITLE:
Seasonal Based Electricity Demand Forecasting Using Time Series Analysis
AUTHORS:
T. M. Usha, S. Appavu Alias Balamurugan
KEYWORDS:
WEKA Time Series Forecasting, SMO Regression, Linear Regression, Gaussian Regression, Multilayer Perceptron
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.10,
August
25,
2016
ABSTRACT: Consumption of the
electric power highly depends on the Season under consideration. The various
means of power generation methods using renewable resources such as sunlight,
wind, rain, tides, and waves are season dependent. This paves the way for
analyzing the demand for electric power based on various Seasons. Many
traditional methods are utilized previously for the seasonal based electricity
demand forecasting. With the development of the advanced tools, these methods
are replaced by efficient forecasting techniques. In this paper, a WEKA time
series forecasting is being done for the electric power demand for the three
seasons such as summer, winter and rainy seasons. The monthly electric
consumption data of domestic category is collected from Tamil Nadu Electricity
Board (TNEB). Data collected has been pruned based on the three seasons. The
WEKA learning algorithms such as Multilayer Perceptron, Support Vector Machine,
Linear Regression, and Gaussian Process are used for implementation. The Mean
Absolute Error (MAE) and Direction Accuracy (DA) are calculated for the WEKA
learning algorithms and they are compared to find the best learning algorithm.
The Support Vector Machine algorithm exhibits low Mean Absolute Error and high
Direction Accuracy than other WEKA learning algorithms. Hence, the Support
Vector Machine learning algorithm is proven to be the WEKA learning algorithm
for seasonal based electricity demand forecasting. The need of the hour is to
predict and act in the deficit power. This paper is a prelude for such activity
and an eye opener in this field.