Short-term Electric Load Forecasting Using Data Mining Technique

Abstract

-In this paper, we introduce data mining techniques for short-term load forecasting (STLF).First, we use the K-mean algorithm to classify historical load data by season into four patterns. Second, we use the k-NN algorithm to divide the classified data into four patterns for Mondays, other weekdays, Saturdays, and Sundays. The classified data are used to develop a time series forecasting model. We then forecast the hourly load on weekdays and weekends, excluding special holidays. The historical load data are used as inputs for load forecasting. We compare our results with the KEPCO hourly record for 2008 and conclude that our approach is effective.

Optimal Scheduling and Real-Time Control Schemes of Battery Energy Storage System for Microgrids Considering Contract Demand and Forecast Uncertainty

“…The ARIMA model is adopted for load forecasting. The effectiveness of ARIMA-based forecasts has already been demonstrated in several studies [20,21]. However, one disadvantage of the ARIMA-model-based short-term forecast is the heavy dependence on the latest historical data.…”

A data mining based load forecasting strategy for smart electrical grids

“…To argue the effectiveness of our load forecasting, our strategy is compared against some of the recently used load forecasting methods, which are; Improved ARIMAX [12], KNN [13], ANNs model [14], and K-mean &KNN [15]. All the proposed capabilities are employed in our load forecasting strategy, hence, DBOR is used for outlier rejection, UHFS is employed for feature selection, and KN 3 B is used for load estimation.…”

“…In addition, electrical load data sets usually contain bad data (e.g., outliers) whose behavior is very exceptional. Recently, several LF strategies for SEG have been introduced, which employ data mining techniques, such as Improved ARIMAX, KNN, ANNs model, and K-mean [12][13][14][15]. In spite of their effectiveness, recently introduced LF strategies still suffer from overfitting and high time and computational penalties.…”

Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network

“…The forecasting accuracy is closely related to the grid running safety and economy [1,2]. At present, many scholars and experts have already done a lot of theoretical researches and practical simulations on shortterm load forecasting [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. More common short-term load forecasting models include regression prediction model [3], time series prediction model [4], artificial neural network prediction model [5], fuzzy logic and expert system [6,7], the wavelet analysis model [8], chaos theory [9], and combination prediction model [10][11][12].…”