Whether it be long-term, like year-ahead, or short-term, such as hour-ahead or day-ahead, forecasting of electricity demand is crucial for the success of deregulated electricity markets. The stochastic nature of the demand for electricity, along with parameters such as temperature, humidity, and work habits, eventually causes deviations from expected demand. In this paper, we propose a feedback-based forecasting methodology in which the hourly prediction by a Fourier series expansion is updated by using the error at the current hour for the forecast at the next hour. The proposed methodology is applied to the Turkish power market for the period 2012–2017 and provides a powerful tool to forecasts the demand in hourly, daily and yearly horizons using only the past demand data. The hourly forecasting errors in the demand, in the Mean Absolute Percentage Error (MAPE) norm, are 0.87% in hour-ahead, 2.90% in day-ahead, and 3.54% in year-ahead horizons, respectively. An autoregressive (AR) model is also applied to the predictions by the Fourier series expansion to obtain slightly better results. As predictions are updated on an hourly basis using the already realized data for the current hour, the model can be considered as reliable and practical in circumstances needed to make bidding and dispatching decisions.

无论是长期的（例如提前一年）还是短期的（例如提前小时或提前一天），对电力需求的预测对于放松管制的电力市场的成功都是至关重要的。电力需求的随机性以及温度，湿度和工作习惯等参数最终导致与预期需求的偏差。在本文中，我们提出了一种基于反馈的预测方法，其中通过使用当前时间的误差来更新下一小时的预测，从而更新傅立叶级数展开的每小时预测。拟议的方法适用于2012-2017年期间的土耳其电力市场，并提供了一个强大的工具，可以仅使用过去的需求数据来预测每小时，每天和每年的需求。需求中每小时的预测误差，在平均绝对百分比误差（MAPE）规范中，提前小时为0.87％，提前一天为2.90％，提前一年为3.54％。自回归（AR）模型也通过傅里叶级数展开应用于预测，以获得更好的结果。由于使用当前小时已实现的数据按小时更新预测，因此在做出投标和调度决策所需的情况下，该模型可以被认为是可靠且实用的。