Fuzzy Modeling to Forecast an Electric Load Time Series

Abstract

This paper tests and compares two types of modelling to predict the same time series. A time series of electric load was observed and, as a case study, we opted for the metropolitan region of Bahia State. The combination of three exogenous variables were attempted in each model. The exogenous variables are: the number of customers connected to the electricity distribution network, the temperature and the precipitation of rain. The linear model time series forecasting used was a SARIMAX. The modelling of computational intelligence used to predict the time series was a Fuzzy Inference System. According to the evaluation of the attempts, the Fuzzy forecasting system presented the lowest error. But among the smallest errors, the results of the attempts also indicated different exogenous variables for each forecast model.

Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

“…Forecasting techniques employed to load prediction can be classified into two primary categories, including traditional statistical methods and artificial intelligent (AI) based methods. Traditional statistical methods, widely used in load prediction, contain the Auto-Regressive-Integrated-Moving-Average (ARIMA) models [20][21][22][23][24], time series regression models [25][26][27][28], fuzzy models [29][30][31][32], and grey prediction models [33][34][35][36]. AIbased methods are generally adaptive and robust to non-stationary data and make the prediction results with high accuracy.…”

Using PSO and Genetic Algorithms to Optimize ANFIS Model for Forecasting Uganda’s Net Electricity Consumption

“…They conclude that the performance of neuro-fuzzy system was better than that of neural networks and Autoregressive Integrated Moving Average (ARIMA) models. The number of customers connected to the electricity distribution network, the temperature and the precipitation of rain are used in [8] as exogenous variables for Seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) and ANFIS models to forecast electric load time series. The ANFIS model gave lower forecast error values than the SARIMAX model.…”

“…They conclude that the performance of neuro-fuzzy system was better than that of neural networks and Autoregressive Integrated Moving Average (ARIMA) models. The number of customers connected to the electricity distribution network, the temperature and the precipitation of rain are used in [8] [11] developed and compared linear regression, artificial neural networks and ANFIS models for load prediction and found that ANFIS model gave more accurate results. For Canada's Ontario province, [12] used ANFIS to model electricity demand using data from the year 1976-2005.…”

A state of the art review on the prediction of building energy consumption using data-driven technique and evolutionary algorithms

“…As shown in Figure 1 (left), there are 30 papers (30%) for national scale’s load prediction 14–16 and 24 papers’ predictions (24%) are at city and region scale. 17–19 It is recognized that most prediction models (46 papers, 46%) were used for energy prediction at building scale. Further, five building categories are classified, e.g., commercial, residential, educational and research, sports and simulation-based building types as shown in Figure 1 (right).…”