Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

Abstract

In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval.

Deep Forest Regression for Short-Term Load Forecasting of Power Systems

“…To obtain more accurate forecasting performance, numerous intelligent forecasting algorithms based on the machine learning of artificial intelligence have been employed to the STLF after 1980s [9]. For example, improved exponential smoothing grey model [10], self-adaptive evolutionary fuzzy model [11], and, artificial neural network (ANN) [12] have been employed into solve the STLF. Furthermore, random forest has been applied into day-ahead load forecasting [13]; deep neural networks (DNNs) have been employed into building energy load forecasting [14]; Levenberg-Marquardt method has been applied into the detection of illegal consumers [15]; the ANN-based conjugate gradient with Powell/Beale restarts has been modeled for fault detection of partial shaded photo-voltaic modules [16]; rule-based autoregressive moving average method has been proposed for forecasting load on a special France days [17] and complex nonlinear systems [18].…”

Electric power load forecasting on a 33/11 kV substation using artificial neural networks

“…Short term electric power load forecasting is a very critical issue for proper operation and dispatch of power system in order to prevent frequent power failures. It is an important prerequisite for economic dispatch of generation units in power plants [4]. The short term active power load forecasting technique must be more accurate so that it helps users to select more optimal power consumption scheme, to minimize production cost and to optimize resources of power system [5].…”

Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

“…In order to meet the needs of rapid social development, the power system has gradually turned into a self-healing, largescale renewable energy access, economic, and efficient smart grid. Load forecasting becomes the basis of smart grid planning and operation [1][2][3]. Short-term load forecasting is important for scheduling plan arrangement, unit combination optimization, and so on [4,5].…”