Electric power load forecasting is essential for the efficient operation and strategic planning of utilities. Decisions regarding the electric market, power generation, load management, and infrastructure development all rely on accurate load predictions. This work presents a novel methodology for day-ahead load forecasting. The approach employs a long short-term memory neural network (LSTM NN) trained on representative load and meteorological data from the region. Before training, the load dataset is grouped by its statistical seasonality through K-means clustering analysis. Clustering load demand, along with similar-day data management, enables more focused training of the LSTM network on uniform data subsets, enhancing the model’s ability to capture temporal patterns and reducing the complexity associated with high variability in demand data. A case study using hourly load demand time-series data provided by the Centro Nacional de Control de Energía (CENACE) is analyzed, and the mean absolute percentage error (MAPE) is calculated, showing lower MAPE than traditional methods. This hybrid approach demonstrates the potential of integrating clustering techniques with neural networks and representative meteorological data from the region to achieve more reliable and accurate regional day-ahead load forecasting.

Short-term load forecasting is essential for the reliable, secure, efficient, and economic operation of modern power systems and electricity markets. Among many forecasting strategies, the similar day (SD) approach for short-term load forecasting was among the earliest used to assess power demand and remains one of the most intuitive and widely adopted techniques worldwide. However, over time, increasing system complexity, richer datasets, and advances in computational intelligence have led to the evolution of SD methodologies beyond heuristic-based rule formulations. This work presents a study of the relevant literature on short-term load forecasting using SD methods reported between 2000 and 2025. This study analyzes how similarity is defined, how forecasts are generated, and how both stages interact within the complete forecasting process in the reviewed literature. Based on these criteria, a unified taxonomy is proposed to classify SD methods into conventional, intelligent, and hybrid formulations. This study provides insight into the methodologies, their performance, and the systems in which they have been tested. The results show that SD-based approaches remain competitive for short-term forecasting and that incorporating artificial intelligence techniques can further enhance their accuracy.

Short-term load forecasting is fundamental for the effective and reliable operation of power systems. Very accurate forecasting methods often involve complex hybrid approaches that combine statistical, physical, and/or intelligent techniques. In this work, we present an innovative, clear, and effective methodology for short-term hourly peak load forecasting that is both simple and highly accurate. The methodology is based on the load forecast used for electricity market purposes, together with fine-tuning dynamic estimation. As a case study, the methodology was applied and tested in Mexico’s interconnected power system. It was implemented across various regions and at both regional and load-\ zone levels of this interconnected power system and, even under a variety of standard and extreme load conditions, achieved outstanding results.

This paper presents the results of a reliability analysis of the generation adequacy of the Mexico's Sistema Interconectado Nacional (SIN), which is comprised of seven interconnected control areas (systems). Analysis was performed taking into account scheduled and forced unit outages, additions of new units, permanent shut down of obsolete units, load demand, additions of new tie lines, and forced outage rates of existing tie lines between areas. 2010 analysis includes the impact of interconnection between Mexico and Guatemala's Power System. Risk indexes -Loss of Load Expectation (LOLE) and the Loss of Energy Expectation (LOEE)- were computed using an analytical (recursive) method and applying the equivalent assisting unit approach. Results of a previous reliability analysis considering SIN as one single area are compared with results considering interconnections between areas.