30-04-2012, 03:14 PM
Short Term Load Forecasting with Fuzzy Logic Systems
Fuzzy logic in Load forecasting .doc (Size: 596 KB / Downloads: 259)
Introduction:
Several papers have proposed the use of Fuzzy Logic for short term load forecasting. At present application of fuzzy method for load forecasting is in the experimental stage. For the demonstration of the method a fuzzy expert systems that forecasts the daily peak load, is selected.
Fuzzy Expert Systems:
The fuzzy system is a popular computing framework based on the concepts of ‘fuzzy set theory’, ‘fuzzy if then rules’ and ‘fuzzy reasoning’. The structure of fuzzy inference consists of three conceptual
components, namely:
Rule Base containing a selection of fuzzy rules.
Database defining the membership functions. These are used in the fuzzy rules.
Reasoning mechanism that performs the inference procedure upon the rules and given
facts and derives a reasonable output or conclusion.
Sometimes it is necessary to have crisp output. This requires a method called De-fuzzification, to extract a crisp value that best represents the fuzzy output. With such crisp inputs and outputs, a fuzzy expert system implements a non-linear mapping from the input space to the output space. This mapping is accomplished by a number of if-then rules, each of which describes a local behavior of the mapping.
Load Forecasting Using Fuzzy Logic.
The Fuzzy Inference systems, unlike neural networks, are applied to peak load and through load forecasting only. The proposed technique for implementing fuzzy logic based forecasting is:
Identification of the day. (Monday, Tuesday etc.,) Lets say we select ‘Tuesday’.
Forecast maximum and minimum temperature for the upcoming Tuesday
Listing the maximum temperature and peak load for the last 10-12 Tuesdays.
For the selected historical data we fit a polynomial.
Let us consider a numerical example. We have the load and temperature data as in the table below :
Conclusion:
The fuzzy logic system may thus be designed to forecast peak and through load. Specific details on the fuzzy logic are dealt in Dr. Keith Holbert’s page at:
There are inherent disadvantages to the system because of the degree of freedom in selecting membership functions, method of fuzzification and de-fuzzification. Such problems may be overcome by combining neural network and fuzzy logic. The neural network optimizes the rule base. This involves the training of the network to the historical data to determine the rules that contribute to a better decision. The network also modifies the initial choice of the membership function to fit the system. One another technique is ‘Genetic Algorithm’. These types of ‘Hybrid’ expert systems are under research.