Short-Term, Medium-Term and Long-Term Load Forecasting: Methods and Applications

Dear Colleagues,

Power system operation and planning rely on forecasting of demand variables such as hourly load, peak load, and total energy. Based on the time horizon, forecasting is categorized as short-term, medium-term, and long-term. Short-term load forecasting (STLF) is the foundation where power system operation is built upon on an intraday and day-ahead basis. Paradigms of applications are unit commitment, hydrothermal coordination, optimal load flow, demand response, and others. Medium-term load forecasting (MTLF) is mostly concerned with fuel import decisions and power unit maintenance scheduling. Long-term load forecasting (LTLF) is exploitable in power system planning.

Until recently, the system operator was responsible for providing the official predictions for the national system level. However, due to the deregulation and increase of competition of modern-day power markets, the strategic actions of various entities such as generation companies, retailers, aggregators, and others rely on accurate load predictions. Moreover, a robust forecasting model for a prosumer would lead to the optimal management of the resources, i.e., energy management, generation, and storage.

Further, the power system gradually transforms into a smart grid where the focus is mostly in small scale instead of system wide level loads such as loads of residences, buildings, distribution buses, and others. Another aspect of smart grid is smart metering, where the data are recorded in low time resolution, resulting in the collection of large amounts of data.

Load forecasting models can be, in general, classified into time series models and computational intelligence models. Time series models such as ARMA and ARIMA demand a priori the definition of the structure of the model. On the other hand, computational intelligence models such as neural networks, support vector machines, neurofuzzy systems, bio-inspired algorithms, and others are trained by the data, and the structure is determined after training. To overcome the drawbacks of each model, hybrid models have been proposed that combine models of a different type or a model and a data preprocessing technique. In addition, deep learning is a new and promising trend in machine learning that has not sufficiently been tested in load forecasting studies.

In the context of these challenges, the main scope of this Special Issue is to develop new methods applicable in short-, medium-, and long-term load forecasting. State-of-the-art papers together with innovative case studies are invited. Multidisciplinary research and cutting-edge approaches are welcomed in order to address the challenges that are raised by modern power systems, smart grids, and competitive power markets.

Asst. Prof. Dr. Ioannis Panapakidis
Guest Editor

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