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"Short-term load forecasting (STLF)" is increasingly significant because of the extensive use of distributed energy resources, the incorporation of intermitted RES, and the implementation of DSM. This paper provides a novel ensemble forecasting model with wavelet transform for the STLF depending on the decomposition principle of load profiles. The model can effectively capture the portion of daily load profiles caused by seasonal variations. The results indicate that it is possible to improve STLF accuracy with the proposed method. The proposed approach is tested with the data taken from Ontario's electricity market in Canada. The results show that the proposed technique performs well in-terms of prediction when compared to existing traditional and cutting-edge methods. The performance of the model was validated with different datasets. Moreover, this approach can provide accurate load forecasting using ensemble models. Therefore, utilities and smart grid operators can use this approach as an additional decision-making tool to improve their real-time decisions. [ABSTRACT FROM AUTHOR]

• This paper is focused on predicting price signals for energy arbitrage of storage systems. • A methodology is presented based on feature selection tools and classification techniques. • A Li-Ion battery in the Ontario competitive electricity market is considered as a case study. • Results indicate that the proposed methodology outperforms alternative point forecasting models. When participating in a competitive electricity market, energy storage systems could deliver various services and stack multiple revenue streams. One potential venue for gaining profit is energy arbitrage, i.e., to harvest the price differential that might exist in some markets between the peak and off-peak hours. To take advantage of price arbitrage, however, it is necessary to have an insight into the price fluctuations of upcoming hours. In this paper, we propose a method for generating predictive electricity price signals to help storage operators make arbitrage decisions. The proposed method delivers signals that are integrated into an optimization platform to schedule the arbitrage operation of a storage system. We examine a Lithium-ion battery as the choice of storage system and the Ontario's competitive electricity market in Canada is considered as the case study. We compare the performance of the proposed approach against the cases that alternative point forecasts of the electricity price are used for self-scheduling of the storage system. The results show that the obtained arbitrage profit of the storage system can be increased significantly using the proposed approach. [ABSTRACT FROM AUTHOR]

Area feeder long-term load forecast (LTLF) is one of the most critical forecasting tasks in electric distribution utility companies. Cost effective system upgrades can only be planned out based on accurate feeder LTLF results. However, the commonly used top-down and bottom-up LTLF methods fail to combine area and feeder information and cannot effectively deal with component-level LTLF. The previous research effort on hybrid approach that aims to combine top-down and bottom-up approaches is very limited. The recent work only focuses on the forecast of the next one-year and uses a one-fit-all model for all area feeders. In response, this paper proposes a novel selective sequence learning method that can convert a multi-year LTLF problem to a multi-timestep sequence prediction problem. The model learns how to predict sequence values as well as the best-performing sequential configuration for each feeder. In addition, unsupervised learning is introduced to automatically group feeders based on load compositions ahead of learning to further enhance the performance. The proposed method was tested on an urban distribution system in Canada and compared with many conventional methods and the existing hybrid forecasting method. It achieves the best forecasting accuracy measured by three metrics AMAPE, RMSE and R-squared. It also proves the feasibility of applying sequence learning to multi-year component-level load forecast. • Converts a multi-year LTLF problem to a multi-timestep sequence prediction problem. • Can incorporate area and feeder level information in multiple historical years. • Can be used to forecast multiple years in the future. • Best sequential configurations can be automatically learned for different feeders. • Unsupervised learning is adopted to further enhance forecasting accuracy. [ABSTRACT FROM AUTHOR]