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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.

Throughout the world, all industries, hospitals, and educational institutions utilize electric power. Typically, electric power is their primary power source. 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. Power plants are the major source of this primary power generation. Different energy sources, including thermal, nuclear, wind, and hydrological are installed to meet the energy demand required in the future. But the successful installation of power plants is a long-term process, and after installation the capacity of the power plant cannot be increased. This leads to the necessity of forecasting as the initial process in power system engineering. Demand forecasting plays a vital role in electricity generation. In recent years, 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. Therefore, this research may be helpful to avoid this critical situation. 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. These changes induced the need to find the way of improvising the power distribution through forecasting the power need and looking for the ways to acquire them. For forecasting the power need, it is not eventually distributed throughout the period, but it varies seasonally. Season have a higher impact in the electric power consumption in the domestic market. The domestic sector electricity consumption varies with respect to rural and urban segments and climatic seasonal variations.

In this paper, seasonal electricity demand forecasting is developed to predict the electricity demand by integrating with the WEKA time series forecasting. Demand forecasting is the process of predicting the needed electric energy in advance. Many prediction and forecasting methodologies were developed in the electricity domain. The main intention of this work is to forecast the electricity demand by using with seasonal data approach. At first, the electric energy monthly consumption data is collected from TNEB office. Time series forecasting is done with Seasonal data of the seasons such as Summer, Winter and Rainy with the WEKA Learning Algorithms. The accuracy parameters are used to measure the performance of the learning algorithms. The learning algorithm Support vector machine is shows the better accuracy compared with other learning algorithms. Finally, the future electricity demand is forecasted for the years from 2016 to 2018 with the help of support vector machine learning algorithm.

The rest of the paper is organized as follows: Section 2 illustrates some of the existing works related to electricity demand forecasting. Section 3 gives the detailed description of the overall flow of the proposed electricity demand forecasting model. Section 4 presents the results evaluation and comparison results of the proposed system. Finally, this paper is concluded and the future work to be carried out is stated in Section 5.

Bresfelean, V.P. [

Hong [

Season based electricity demand forecasting model is designed to forecast the seasonal based electricity power consumption.

the Monthly Electric Consumption Data of Domestic Category is collected from TNEB. The power consumption by the Domestic category is highly influenced by the seasons. So here prediction is highly recommended for the Seasonal forecasting. This data is split into three dataset based on the seasons. Here the three seasons such as Summer, Winter and Rainy are considered for the forecasting. This model is implemented with WEKA Time series forecasting. In WEKA packet manager, Time Series Forecasting package is available. The WEKA learning algorithms such as Multilayer Perceptron, Support Vector Machine, Linear Regression, and Gaussian Pro- cess are capable of predicting the numeric quantity. They are used for this comparative study for forecasting electricity consumption based on seasonal data.

The monthly electric power consumption of the domestic category of Madurai District Data is used as the sample to deploy the forecasting. This Dataset is collected from Tamil Nadu Electricity Board (TNEB) [

This research used data set [

Season | Period in Months |
---|---|

Winter | November |

December | |

January | |

Summer | February |

March | |

April | |

May | |

June | |

Monsoon/Rainy | July |

August | |

September | |

October |

The missing value may reduce the accuracy of forecast. So the missing value is filled with the methods such as mean, median or previous value. In this dataset, the missing value is filled with the previous value in the time series.

Waikato Environment for Knowledge Analysis-version 3.7.6 (Weka 3.7.6) tool [

There are two configurations in WEKA Forecasting. They are named “Basic configuration” and “Advanced configuration”. In Basic configuration, the important parameters such as timestamp, periodicity and number of units to be forecasted are available. A timestamp is a sequence of information which identifies an event, and usually it is represented as date and time of day, sometimes accurate to a small fraction of a second. Since the used datasets are seasonal data, the time stamp is set as “Use Artificial Time Index”. This is used only when we are adjusting for trends via a real or artificial time stamp. That means that it will increment the artificial time value with time stamp. In Advanced configuration, the Base learner is available; it has configured parameters specific to the learning algorithm selected. Here the WEKA learning algorithms such as Multilayer Perceptron [

The following shows the structure of ARFF file.

@relation 'Time Series Data'

@attribute Period date 'MMM-yyyy'

@attribute Electricity_Consumption numeric

@data

Feb-2008 25796987

Mar-2008 26796987

Apr-2008 29946353

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. WEKA learning algorithms identify the patterns of the each seasonal data. Once these patterns are identified, then we can carry over forecasting. Finally, the forecasted power consumption is computed with the help of WEKA learning algorithms. The accuracy parameters such as Mean absolute error, Root mean squared error, and Direction accuracy are calculated. These comparison results shows that support vector machine algorithm gives less error with more direction accuracy compared to other WEKA learning algorithm.

where

In the case of a set of n values

where A_{t}, A_{t} is the actual value at time t and F_{t} is the forecast value at time t. Variable N represents number of forecasting points. The function sign is sign function.

The main aim of forecasting is to predict the future value of some time series. Seasonal based electricity demand forecasting is important for managing activities of consumers. Generally, seasonal based demand forecasting is based on the regularities in time series related to the changes in seasons. In this work, we use a real time monthly electric power consumption of the domestic category of Madurai District Data is used as the sample to deploy the forecasting. This Dataset is collected from Tamil Nadu Electricity Board (TNEB) [

From the analysis of the seasonal dataset, the result shows that, the support vector machine learning algorithm produces low Mean Absolute Error with increased Direction Accuracy. Thus the upcoming power consumption of three seasons is forecasted are as shown in the Figures 8-10.

The purpose of this research was to find a suitable model to forecast the seasonal based electric power consumption in a domestic category. 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. This shortage is because of the failure in the needed power forecasting. Using the WEKA Time series forecasting package, the Learning Algorithms such as Multilayer Perceptron, Support Vector Machine, Linear Regression, and Gaussian Process are applied to the electric power consumption from December 2006 to November 2010. The time series forecasting is used to identify the patterns hidden in the data set. Once these patterns are identified using WEKA learning algorithms, and then we can carry over forecasting. The accuracy parameters such as Mean Absolute Error and Direction Accuracy of the learning algorithms are calculated. The best forecasting method is chosen

by considering the low Mean Absolute Error and high Direction accuracy. Hence, the Support Vector Machine is found to be the best technique for the electricity demand prediction for seasonal based dataset. The upcoming power consumption of three seasons is forecasted for three years using support vector machine learning algorithm. These seasonal forecasts provide information about how the electric energy demand is likely to be for three years into the future. These forecasts will provide alerts to government and will helpful in making decisions for generating power through seasonal based power plants.

T. M. Usha,S. Appavu Alias Balamurugan, (2016) Seasonal Based Electricity Demand Forecasting Using Time Series Analysis. Circuits and Systems,07,3320-3328. doi: 10.4236/cs.2016.710283