Short-Term Load Forecasting through the Identification of Similar Hour Series

The paper introduces a novel method for short-term load forecasting (STLF) when there is insufficient historical data to train a similar-day-based algorithm. The proposed approach involves constructing a virtual historical day using historical hour arrays, thus improving the algorithm's ability to forecast accurately. Relevant similarity factors can be used to identify similar series of hours, although their impact on power consumption can vary depending on different factors such as geographical location, power network infrastructure, season, and population habits. The proposed model starts with a range of factors and calculates the impact of the significant contributing factors on power consumption. The model allows for the optimal selection of similar hours, even in cases where it may be challenging to determine the most relevant factors and their impact on the forecast in advance. To improve the accuracy of the STLF forecast, the model uses a genetic algorithm (GA) to optimize the impact of each similarity factor. Experimental results presented in the paper demonstrate the potential of this method to enhance the accuracy of the STLF forecast.