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• A novel CElif system is proposed in this paper. • Decomposition and denoising strategy is designed in the CElif system. • The MSsa mechanism is superior both in search ability and operation efficiency. • The construction of the five ANNs covers the inherent modes of sequence. • Differences in people's behavior on weekdays and weekends are considered. Under the trend of the global green and low-carbon transformation, accurate prediction of electric load is an urgent problem for all countries to maintain the normal operation of production and living activities. Currently, a large number of models based on single-point forecasting of electric load have emerged. However, these models are limited by the high randomness of electric load data, which restricts the further improvement of prediction accuracy. To fill this gap, a CElif (Combined Electric Load Interval Forecasting) system is proposed in this paper, which contains decomposition and denoising module, individual forecasting module, optimization module and evaluation module, aiming to tolerate uncertainty to provide policymakers with more information. Decomposition and denoising strategy is adopted to extract and reconstruct the inherent modes of the original sequence. In order to verify the superiority of the CElif system, the electric load data of 30-minute interval in Australia are used to test. The numerical results conclude the CElif system not only has excellent coverage performance in electric load interval prediction, controls the uncertainty to a great extent, but also provides a basis for power system dispatching management. [ABSTRACT FROM AUTHOR]

• A novel integrated short-term power load forecasting system is proposed. • The fuzzy granulation technique simplifies the large-scale dataset and extracts the effective characteristics of the raw data. • A novel multi-objective variant of the tuna swarm optimizer is utilized to integrate multiple forecasting sequences. • The optimal weight coefficients of the ensemble model are theoretically demonstrated to be the Pareto optimal solutions. • Our proposed forecasting system can gauge the uncertainty of future electricity load. As an irreplaceable power source, electricity is responsible for sustaining the national economy and social development, and the precondition for the power system's stable operation is to perform an accurate short-term load forecast (STLF). However, with the increasing forms of social power consumption and the emergence of large-scale sustainable resources on the grid, which make STLF increasingly challenging as the power load exhibits greater stochasticity and instability. Therefore, a novel STLF system is developed in this paper, which incorporates data fuzzy granulation, a high-performance optimizer for integrating forecasting sequences, point and interval forecasts. Moreover, the performance tests of the optimization algorithm verify that our proposed optimizer can obtain more approximate solution sets to the real Pareto front and outshines the traditional optimization algorithm concerning convergence and diversity. Load data from three regions of Australia demonstrate that our developed system can remarkably contribute to the accuracy and stability of the STLF , and also quantify the volatility and uncertainty of the power load, which allows power workers to better capture the fluctuation interval of future loads and effectively enhance the flexibility of grid operation. [ABSTRACT FROM AUTHOR]

• A novel CTSGAN deep learning framework is first proposed and applied to model day-ahead electricity price scenarios. • The CTSGAN point forecasting model can eventually be transformed into a probabilistic forecasting model by enhancing the diversity of random input. • The CTSGAN probabilistic forecasting model can directly yield high-quality forecasting intervals with different coverage probabilities as a multi-objective forecasting model. • PSO-based optimal conditions selection method can increase the forecasting accuracy. Electricity prices in spot markets are volatile and can be affected by various factors, such as generation and demand, system contingencies, local weather patterns, bidding strategies of market participants, and uncertain renewable energy outputs. Because of these factors, electricity price forecasting is challenging. This paper proposes a scenario modeling approach to improve forecasting accuracy, conditioning time series generative adversarial networks on external factors. After data pre-processing and condition selection, a conditional TSGAN or CTSGAN is designed to forecast electricity prices. Wasserstein Distance, weights limitation, and RMSProp optimizer are used to ensure that the CTGAN training process is stable. By changing the dimensionality of random noise input, the point forecasting model can be transformed into a probabilistic forecasting model. For electricity price point forecasting, the proposed CTSGAN model has better accuracy and has better generalization ability than the TSGAN and other deep learning methods. For probabilistic forecasting, the proposed CTSGAN model can significantly improve the continuously ranked probability score and Winkler score. The effectiveness and superiority of the proposed CTSGAN forecasting model are verified by case studies. [ABSTRACT FROM AUTHOR]