###### Abstract  
Accurate and reliable power load forecasting not only takes an important place in management and steady running of smart grid, but also has environmental benefits and economic dividends. Accurate load point forecasting can provide a guarantee for the daily operation of the power grid, and effective interval forecasting can further quantify the uncertainty of power load on this basis to provide dependable and precise load information. However, most of the previous work focuses on the deterministic point prediction of power load and rarely considers the interval prediction of power load, which makes the prediction of power load not comprehensive. In this study, a new double hybrid load forecasting system including point forecasting module and interval forecasting module is developed, which can make up for the shortcomings of incomplete analysis for the existing research. The point forecasting module adopts a nonlinear integration mechanism based on Back Propagation (BP) network optimized by Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) to improve the accuracy of point prediction. A fuzzy clustering interval prediction method based on different data feature classification is successfully proposed which provides an effective tool for load uncertainty analysis. The experiment results show that the system not only has a good effect in accurately predicting power load, but also can analyze the uncertainty of the power load, which can be used as an effective technology of power system planning. p 20202019 February 20202024 February 202020 February 202020 February 202020 February  
achieve precise and steady STLF, researchers have adopted a lot of methods, including (a) statistical method, (b) artificial intelligence method and (c) hybrid method [10]. In the early stage of power load forecasting, traditional statistical methods are often applied, including some conventional forecasting methods, such as regression method [11], exponential smoothing [12], Autoregressive Moving Average Model (ARMA) [13], Autoregressive Integrated Moving Average (ARIMA) [14,15], seasonal ARIMA [16], grey forecasting model (GM) [17], etc. These models could obtain power load prediction, but due to their own limitations, they cannot achieve the expected forecasting accuracy. In order to overcome these limitations, more and more effective load forecasting approaches have been put forward. In recent years, the prediction model based on artificial intelligence is gradually springing up in electric load prediction [18]. At the moment, the algorithms of artificial intelligence mainly include artificial neural network (ANN) [19,20], support vector machine (SVM) [21], multi-layer perceptron (MLP) and radial basis function (RBF). Although better than the traditional methods, they still unable to fit the current complicated and variable power load characteristics well to achieve satisfactory accuracy due to the defects of a single prediction method [22]. For example, the artificial neural network is easy to fall into local optimization, over fitting and low convergence rate [23]. This has led to the largely establishment of integrating and hybrid models, which are composed of several single models and can achieve better prediction performance [24]. [PERSON] et al. [25] successfully proposed a new power load prediction model, CS-SSA-SVM, which integrated singular spectrum analysis (SSA), support vector machine (SVM) and cuckoo search (CS) algorithm. This model can significantly enhance the effectiveness of power load forecast. [PERSON], [PERSON] et al. [26] developed a short-term load prediction model using a unit for feature learning named Pyramid System and recurrent neural networks, and it can greatly increase the stabilization and safety of the smart grid. [PERSON], [PERSON] et al.