M. Lopez

• Miguel Hernández University of Elche

Accurate prediction of electrical demand is crucial for the efficient operation of power systems. However, the unprecedented activity restrictions imposed during the pandemic led to unforeseen disruptions in electrical consumption, challenging the predictive capabilities of existing systems. This phenomenon was widespread, affecting power systems g...

Due to the infeasibility of large-scale electrical energy storage, electricity is generated and consumed simultaneously. Therefore, electricity entities need consumption forecasting systems to plan operations and manage supplies. In addition, accurate predictions allow renewable energies on electrical grids to be managed, thereby reducing greenhous...

Electrical energy is consumed at the same time as it is generated, since its storage is unfeasible. Therefore, short-term load forecasting is needed to manage energy operations. Due to better energy management, precise load forecasting indirectly saves money and CO2 emissions. In Europe, owing to directives and new technologies, prediction systems...

Electricity demand presents a repetitive pattern following daily, weekly and seasonal patterns. However, factors like temperature or social events tend to disrupt these patterns introducing outlying data that is difficult to forecast. This paper introduces a new methodology to classify special days without any prior knowledge of the database. Simpl...

This paper introduces a new methodology to include daylight information in short-term load forecasting (STLF) models. The relation between daylight and power consumption is obvious due to the use of electricity in lighting in general. Nevertheless, very few STLF systems include this variable as an input. In addition, an analysis of one of the curre...

Daylight Saving Time (DST) policies have been in use since early in the 20th century. However, their energy saving effect is under review. The generalization of LED lighting has reduced the impact of lighting energy on the total energy consumption and, therefore, the effect of DST has been reduced. Nevertheless, in order to design an effective new...

Short-Term Load Forecasting is a very relevant aspect in managing, operating or participating an electric system. From system operators to energy producers and retailers knowing the electric demand in advance with high accuracy is a key feature for their business. The load series of a given system presents highly repetitive daily, weekly and yearly...

Short-Term Load Forecasting is a very relevant aspect in managing, operating or participating an electric system. From system operators to energy producers and retailers knowing the electric demand in advance with high accuracy is a key feature for their business. The load series of a given system presents highly repetitive daily, weekly and yearly...

Artificial Intelligence (AI) has been widely used in Short-Term Load Forecasting (STLF) in the last 20 years and it has partly displaced older time-series and statistical methods to a second row. However, the STLF problem is very particular and specific to each case and, while there are many papers about AI applications, there is little research de...

This paper presents the implementation of a new online real-time hybrid load-forecasting model based on an autoregressive model and neural networks. This new system is currently running at the Spanish Transport System Operator (REE) and provides an hourly forecast for the current day and the next nine days timely every hour for the national system...

The participation of demand response in energy and operation markets is gaining interest in the last years due to the necessity of integrating renewable resources in the power systems. The potential of small demand segments is very high, but its complexity (thousands of customers to achieve the same potential that large customers) and costs (for in...

The participation of Demand Response in energy and operation markets is gaining interest in the last years due to the necessity of integrating renewable resources in the Power Systems. The potential of small demand segments is very high, but its complexity (thousands of customers to achieve the same potential that large customers) and costs (for in...

Short-Term Load Forecasting (STLF) has been a relevant research topic for over two decades now. However, it is an ongoing process since the behavior of consumers and producers continue changing as new technologies and new policies become available. This paper presents the results of a research study for the Spanish Transport System Operator (REE) w...

In Psycholinguistics and Computational Linguistics free association tasks have been used commonly as general data collection to develop semantic networks that simulate semantic space of the speakers of a language. Semantic networks incorporate measures such as number of associates, associative strength and other groups of quantitative features of d...

In Psycho linguistics and Computational Linguistics free association tasks have been used commonly as general data collection to develop semantic networks that simulate semantic space of the speakers of a language. Semantic networks incorporate measures such as number of associates, associative strength and other groups of quantitative features of...

This paper proposes the use of two indicators of the predictability of the load series along with an accuracy value such as mean average percentage error as standard measures of load forecasting performance. Over the last 10 years, there has been a significant increase in load forecasting models proposed in engineering journals. Most of these model...

The field of short-term load forecasting (STLF) has drawn wide attention over the last decade and numerous models have been proposed in the last 5 years. Different techniques have been developed and tested spanning from statistical methods to neural network structures or genetic algorithms and other forms of Artificial Intelligence (AI). STLF is a...

The use of neural networks in load forecasting has been a popular research topic over the last decade. However, the use of Kohonen's self-organizing maps (SOM) for this purpose remains yet mostly unexplored. This paper presents a forecasting model based on this particular type of neural network. The scope of this study is not only to prove that SOM...

This paper proposes the use of an indicator of the predictability of the load series along with an accuracy value such as Mean Average Percentage Error as standard measures of load forecasting performance. Over the last 10 years, there has been a significant increase in load forecasting models proposed in engineering journals. Most of these models...

There has been a significant production of load forecasting models over the last 5 years. These models present a wide variety of techniques, most of them using novel artificial intelligence approaches. Load forecasting is a complex matter and it is the result of several processes that, depending on the database, may be of more or less importance. H...

The study presented in this paper used Kohonen's Self-Organized Maps, which is one of the more uncommon techniques based on neural networks in load forecasting. The aim of this study is not only to show that this technique is capable of producing accurate short-term load forecasting results which should not be neglected, but also to provide a deep...

An artificial neural network based on Kohonen self- organizing maps (SOM) and its application to short-term load forecasting (STLF) is presented. The proposed model is capable of forecasting up to 24 hour long profiles, up to 24 hours ahead of the beginning of the period. The input used by the model depends on the available information at the time...