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.
Highlights
Throughout the world, all industries, hospitals, and educational institutions utilize electric power
This section explains about the results and discussions, including the comparative analysis of the accuracy parameters such as Mean absolute error, Root mean squared error, and Direction accuracy of the WEKA learning algorithms available
Power layoff is the important problem faced by the government, industries, business people and the residents during very peak summer, winter and in monsoon season
Summary
Throughout the world, all industries, hospitals, and educational institutions utilize electric power. If there is a shortage in this primary power, all of these entities would collapse This situation must be avoided, and this problem can be solved by electric energy demand forecasting. The state of Tamil nadu in India is facing irregular power supply to all its districts due to a shortage of power This problem is the consequence of a lack of forecasting methodologies. Power distribution is a cumbersome task due to the increase in the consumption of electric power by the increase in the usage of electronic equipment and by the modernizations and the new entry of industries. The domestic sector electricity consumption varies with respect to rural and urban segments and climatic seasonal variations
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