The Effectiveness of Hybrid Backpropagation Neural Network Model and TSK Fuzzy Inference System for Inflation Forecasting

Forecasting may predict the accurate future condition based on the previous circumstance. Problems that may occur are related to forecasting accuracy. This study proposes a combination of two methods: Neural Network (NN) and Fuzzy Inference System (FIS) to accuratelly forecast the inflation rate in Indonesia. Historical data and four external factors were used as system parameters. The external factors in this study were divided into two fuzzy sets. While time series variables were divided into three fuzzy sets. The combination of them generated a lot of fuzzy rules that may reduce the forecasting effectiveness. As a consequence, the less fit fuzzy rules formation would produce a low accuracy. Therefore, grouping all input variables into positive parameters and negative parameters are necessary for efficiency improvement. To evaluate the forecasting results, Root Means Square Error (RMSE) analytical technique was used. Fuzzy Inference System Sugeno was used as the base line. The results showed that the combination of the proposed method has better performance (RMSE=2.154901) than its base line

Characterisation of large changes in wind power for the day-ahead market using a fuzzy logic approach

Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution Numerical Weather Prediction (NWP) models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power out- put that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using Genetic Programming (GP) and Quantile Regression Forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented using colour maps, which provide a useful way to interpret the characteristics of the ramp events