Effect of Triangular and Gaussian Membership Functions in Fuzzy Time Series Forecasting.
Abstract
Fuzzy Time (FTS) plays a role in the fuzzification of data, which is based on certain functions. In this , a 24 weeks load data from PHCN was used and fuzzified based on the Gaussian Membership Functions, after that all fuzzified data are defuzzified to get normal form.
The results obtained using the GMF (Gaussian Membership Functions) are compared with that of the TMF (Triangular Membership Function), from which the comparison basis was based on, qualitative performance indicator and statistical error. The RMSE Values obtained using the GMF and the TMF are 66.5 and 17.1 respectively, while their correlation factor R is 0.98 for TMF and 0.86 for GMF.
From the analysis carried out, the TMF generated the least RMSE and hence, is more suitable in forecasting the electric load.
INTRODUCTION
Background
Load forecasting is of vital importance in the electricity industry, especially in a deregulated economy like that of Nigeria. It has many applications including energy purchasing and generation, load switching, contract evaluation, and infrastructural development.
A large variety of mathematical models have been developed and applied in carrying out load forecasting. In this work, the Fuzzy Time Series (FTS) approach is used for load forecasting.
There is a planned Government policy towards unbundling the utility (Power Holding Company of Nigeria (PHCN)) company with the objective of improving the efficiency of electricity generation, transmission, and distribution.
This emphasizes proper and effective planning, management, and operations of the network. The operation and planning of a power utility company require an adequate model for electric power load forecasting. Load forecasting plays a key role in helping an electricity utility to make important decisions on power, load switching, voltage control, network reconfiguration, and infrastructure development.
It is extremely important for optimal of generation and distribution of electric energy to have as precise as possible the load profile prediction. According to Abbasovand Mamedova (2003), time series represents a consecutive series of observations taken over equal time intervals.
The application of Fuzzy Logic and fuzzy sets to time series analysis gave rise to Fuzzy Time Series.
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