Short-term load forecasting based on conditional generating adversarial networks

Hybrid short-term load forecasting using CGAN with CNN and semi-supervised regression

In electrical engineering load forecasting have been tried out using traditional forecasting models and artificial intelligence techniques and have become one of the major research fields. An accurate and efficient Short-term load forecasting (STLF) plays a vital role for economic operational planning of both regulated power systems and electricity markets. To develop a solution/methodology to demand forecast (Hourly load forecast) and by incorporating weather conditions. However, STLF it is pertinent to understand conventional methods. Therefore, popular conventional methods were implemented to learn methods for STLF. This paper presents Simple Moving Average, Weighted Moving Average, Exponential Moving Average, Auto Regressive and Multiple linear regressions for short term load forecasting. Conventional technique approach is implemented on historical load data for forecasting the load. PGVCL hourly load data used for training and testing is collected from ALDC, Jetpur, Gujarat. Hence hereby in this paper I have compared five conventional methods for STLF and have come out with a result that forecasting errors of time series models gives reasonably accurate hour ahead load forecast.

Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction

Short-term load forecasting plays a vital role for the power system’s safe operation and production arrangements. This paper regards electric load forecasting as a nonlinear time series prediction problem, and establishes the electric load autoregressive prediction model (ARX Model) based on historical load data, approaching ARX model parameters with RBF neural network, estimating model parameters with using a Structured Nonlinear Parameters Optimization Method (SNPOM), proposing cycle prediction method for short-term electric load forecasting. Experimental results show that the method has higher prediction accuracy for short-term electric load forecasting. Keywords: Short-term load forecasting; time series; RBF-ARX model; cycle forecasting; structured nonlinear parameter optimization method

Short-term load forecasting is very important for power system dispatching since the economy and stability of power system are affected by the accuracy of load forecasting. Therefore, a short-term load forecasting method based on deep belief neural network (DBN) is proposed in this paper. Firstly, the low-voltage user load data are normalized to construct the training set and test set for low-voltage user short-term load forecasting; then, the DBN model is trained and adjusted by using the training set to obtain the well-trained DBN network; finally, the historical data of short-term load of users are input for load forecasting. The actual load data in Zhejiang power grid of China are serviced for demonstrating the effectiveness the proposed algorithm. The simulation results show that the short-term load forecasting algorithm proposed in this paper is with high accuracy for the actual load forecasting of low voltage users.

Short-term load forecasting is an important component of grid economic dispatching and the forecasting error directly affects the economy of grid operation. Different from wide area power grid, it is more difficult to implement short-term user load forecasting, especially existing missing data. The feature model about user load is constructed by analyzing the static, dynamic and distributed characteristics of user load. Then, based on gradient boosted decision trees and similar training samples, a short-term user load filling and forecasting model is proposed. Through predicting different types of user load in short-term, to analyze filling model about missing load data is helpful to improve the accuracy of short-term user load forecasting. And, the forecasting accuracy and its stability of the proposed model are validated.

A reliable and accurate short-term load forecasting (STLF) helps utilities and energy providers deal with the challenges posed by supply and demand balance, higher penetration of renewable energies and the development of electricity markets with increasingly complex pricing strategies in future smart grids. Recent advances in deep learning have been successively utilized to STLF. However, there is no certain study that evaluates the performances of different STLF methods at an aggregated level on different datasets with different numbers of daily measurements.In this study, a deep learning STLF architecture called Load2Load is proposed for day-ahead forecasting. Different forecasting methods have been evaluated and compared on two datasets with different temporal resolutions and features. An additive ensemble method as well as a selective ensemble method that selects the outputs of different forecasters in an hourly manner are proposed. Moreover; a modified sequential forward feature selection algorithm is proposed, resulting in better performance with a much smaller number of features.Numerical results show that the proposed Load2Load architecture has a competing performance compared to other advanced forecasters. When used together with the proposed ensemble methods, Load2Load can significantly improve the forecasting performance. The proposed feature selection algorithm results in better performance for the majority of the cases while reducing the dimensionality. According to the results with two different datasets; the proposed methods are shown to be robust to temporal resolutions, feature types and sequence lengths.

Accurate short-term load forecasting (STLF) is critical to the operation and control of the power grid. In the literature, deep learning techniques have shown its superiority for accurate load forecasting over other methods due to its strong regression ability. However, the training process of the deep learning systems is time-consuming providing that they contain a large number of parameters due to the complex structure. This paper presents a novel broad learning system (BLS) approach for load forecasting. The BLS, which is established as a flat network, is an alternative to the deep learning system by expanding the structure in a wide sense. The expanded broad structure of BLS ensures its strong approximation capability of nonlinear mapping. Moreover, the BLS has a much faster training process compared to deep learning due to its simple structure. The proposed method is tested with an open dataset at low-voltage level from Australia and the testing results demonstrate the effectiveness of the proposed method.

Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.

Accurate short-term load forecasting is important for the safe and effective functioning of modern power systems. Seasonal-trend decomposition based on LOESS (STL) is an efficient method for handling the intricacy and fluctuation of load data. However, different types of components reflect different levels of information and have different importance in terms of capturing temporal features. Moreover, graph convolutional network (GCN) is often utilized to capture the non-Euclidean spatial features in load data. However, as the number of network nodes increases, the generalization capacity of the GCN decreases. Therefore, a novel spatiotemporal model, namely, multitask GCN with attention-based STL (MG-ASTL), is proposed for accurate short-term load forecasting. First, a new attention-based STL method is proposed, which utilizes attention mechanism to weight different components, thus making the proposed model to focus on more important components for more effective temporal feature extraction. Second, a new multitask GCN method is proposed, which utilizes density-based spatial clustering of applications with noise (DBSCAN) to divide load data into different groups for multitask learning, so that the simple spatial patterns with fewer nodes can be learned to increase the generalization capacity. The effectiveness of the proposed model is validated on the basis of experimental results under different conditions.

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods.

Accurate forecasting of power system short -term load has been one of the most important issues in the electricity industry. And the forecasting accuracy is influenced by many unpredicted factors. Because of the non-linear features of short-term power load, the paper uses support vector machines (SVM) technology for the short-term electricity load forecast. The method can better solve such practical problems as small samples, nonlinearity, high dimensionality and local minimization, which can greatly enhancing its ability to handle non-linear. To solve the problems of SVM in training for large-scale convergence, such as slow convergence, greet complexity, particle swarm optimization(PSO) is proposed for the secondary planning problem to enhance SVM computing speed. The improved SVM is applied to short-term load forecasting, empirical studies show that the method has a high prediction accuracy and faster computing speed.

15 - Forecasting of energy demand in virtual power plants

Power system load is a stochastic and non-stationary process. Due to the influence of various factors, some bad data may exist in the load observation value. These data are mixed into the normal load data to participate in the training of neural network, which seriously affects the accuracy of load forecasting. Short-term load forecasting is the basis of power system operation and analysis, improving the precision of load forecasting is an important means to ensure the scientific decision-making of power system optimization. In order to improve the precision of short term load forecasting in power system, a short-term load forecasting model based on genetic algorithm is proposed to optimize BP neural network. Firstly, using genetic algorithm to optimize the initial weights and thresholds of BP neural network to improve the prediction accuracy of BP neural network; Through the comparison and analysis before and after the model optimization, the experimental results with smaller prediction error were obtained. The simulation results show that the short-term load forecasting model established by this method has faster convergence rate and higher prediction precision.

Electricity load forecast needs to ensure minimum load wastage and requires intelligent decision-making systems to accurately predict future load demand. Learning capability, robustness and ability to solve nonlinear problems make ANN widely acceptable. For accurate short-term load forecast (STLF), an ensemble soft computing approach, namely ANN-PSOHm, composed of an artificial neural network (ANN) and particle swarm optimization (PSO) with homeostasis based mutation is presented in this article. To enhance the learning strength of ANN, PSO undergoes homeostasis mutation to bring greater diversity among solutions by exploring in wider search space. To demonstrate the effectiveness of ANN, three case studies on load data of NEPOOL region (courtesy ISO New England) are performed. The experimental results show that ANN-PSOHm improves accuracy by 11.57% MAPE over ANN-PSO.