An Introduction To Management Science Quantitative Approaches To Decision Making

There is an increment within the power charge of Universiti Tun Hussein Onn Malaysia (UTHM) lately. Thus, there is an ought to anticipate UTHM power utilization precisely to arrange for future vitality demand and utility saving decisions. Past researches on UTHM power utilization prediction have been carried out utilizing time series models, multiple linear regression, 1st-order fuzzy time series (FTS) and seasonal Autoregressive Integrated Moving Average (SARIMA). The 1st-order FTS yields the best accuracy among these four methods. Past forecasting studies showed higher order FTS can yield better accuracy. Thus, the 3rd -order FTS with trapezoidal membership function and 2009 to 2018 of monthly electricity consumption data were utilized to predict 2019 monthly electricity usage. The process of the 3rd -order FTS using trapezoidal membership function was shown in detail using January data. The process was repeated with other months. It was proved that 3rd -order FTS performs the best in overall data, but 2nd-order FTS predicts the future the best.

Third-order fuzzy time series with trapezoidal membership for electricity consumption forecasting

In this paper a hybrid forecasting technique that integrates Cat Swarm optimization Clustering (CSO-C) and Particle Swarm Optimization (PSO) with Fuzzy Time Series (FTS) forecasting is presented. In the three stages of FTS, CSO-C found application at the fuzzification module where its efficient capability in terms of data classification was utilized to neutrally divide the universe of discourse into unequal parts. Then, disambiguated fuzzy relationships were obtained using Fuzzy Set Group (FSG). In the final stage, PSO was adopted for optimization; by tuning weights assigned to fuzzy sets in a rule. This rule is a fuzzy logical relationship induced from FSG. The forecasting results showed that the proposed method outperformed other existing methods; using RMSE and MAPE as performance metrics.

Alhassan Mohammed

Papers

A Hybrid Fuzzy Time Series Technique for Forecasting Univariate Data