Forecasting the electricity consumption is one of the most challenging tasks for energy domain stakeholders. Having reliable electricity consumption forecasts can help minimizing the cost of electricity and also enable a better control on the electricity tariff. This paper presents a study regarding the forecast of electricity consumption using a methodology based on Hybrid neural Fuzzy Inference System (HyFIS). The proposed approach considers two distinct strategies, namely one strategy using only the electricity consumption as the input of the method, and the second strategy uses a combination of the electricity consumption and the environmental temperature as the input. A case study considering the forecasting of the consumption of an office building using the proposed methodologies is also presented. Results show that the second strategy is able to achieve better results, hence concluding that HyFIS is an appropriate approach to incorporate different sources of information. In this way, the environmental temperature can help the HyFIS method to achieve a more reliable forecast of the electricity consumption.

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players' characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

Worldwide electricity markets are undergoing a major restructuring process. One of the main reasons for the ongoing changes is to enable the adaptation of current market models to the new paradigm that arises from the large-scale integration of distributed generation sources. In order to deal with the unpredictability caused by the intermittent nature of the distributed generation and the large number of variables that contribute to the energy sector balance, it is extremely important to use simulation systems that are capable of dealing with the required complexity. This paper presents the Tools Control Center (TOOCC), a framework that allows the interoperability between heterogeneous energy and power simulation systems through the use of ontologies, allowing the simulation of scenarios with a high degree of complexity, through the cooperation of the individual capacities of each system. A case study based on real data is presented in order to demonstrate the interoperability capabilities of TOOCC. The simulation considers the energy management of a microgrid of a real university campus, from the perspective of the network manager and also of its consumers/producers, in a projection for a typical day of the winter of 2050.

Reliable consumption forecasts are crucial in several aspects of power and energy systems, e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators. With this need, several methodologies for electricity forecasting have emerged. However, the study of correlated external variables, such as temperature or luminosity, is still far from adequate. This paper presents the application of the Wang and Mendel's Fuzzy Rule Learning Method (WM) to forecast electricity consumption. The proposed approach includes two distinct strategies, the first one uses only the electricity consumption as the input of the method, and the second strategy considers a combination of the electricity consumption and the environmental temperature as the input, in order to extract value from the correlation between the two variables. A case study that considers the forecast of the energy consumption of a real office building is also presented. Results show that the WM method using the combination of energy consumption data and environmental temperature is able to provide more reliable forecasts for the energy consumption than several other methods experimented before, namely based on artificial neural networks and support vector machines. Additionally, the WM approach that considers the combination of input values achieves better results than the strategy that considers only the consumption history, hence concluding that WM is appropriate to incorporate different information sources.

Most market operators provide daily data on several market processes, including the results of all market transactions. The use of such data by electricity market simulators is essential for simulations quality, enabling the modelling of market behaviour in a much more realistic and efficient way. RealScen (Realistic Scenarios Generator) is a tool that creates realistic scenarios according to the purpose of the simulation: representing reality as it is, or on a smaller scale but still as representative as possible. This paper presents a novel methodology that enables RealScen to collect real electricity markets information and using it to represent market participants, as well as modelling their characteristics and behaviours. This is done using data analysis combined with artificial intelligence. This paper analyses the way players' characteristics are modelled, particularly in their representation in a smaller scale, simplifying the simulation while maintaining the quality of results. A study is also conducted, comparing real electricity market values with the market results achieved using the generated scenarios. The conducted study shows that the scenarios can fully represent the reality, or approximate it through a reduced number of representative software agents. As a result, the proposed methodology enables RealScen to represent markets behaviour, allowing the study and understanding of the interactions between market entities, and the study of new markets by assuring the realism of simulations.The restructuring of Electricity Markets (EM) has created a new design of the electricity sector, becoming a new global trend. The monopoly model gave way to a non-vertical structure that allows the development of a free competition market, as it allows the liberalization of potentially competitive segments, such as production, transportation and distribution [1]. These changes provide conditions for the establishment of a more competitive market but also more complex, where the laws of supply and demand stimulate the quality of service, reduce costs and increase efficiency. The growth of complexity creates new challenges, namely the difficulty in decision making due to markets unpredictability and the high number of associated variables [2].Simulation started to play a key role in supporting decision-making in the electricity sector. It allows studying and analysing the profiles and strategies of market participants and, thus, model market behaviour and forecast proposals [3]. Such characteristics enable the establishment of conditions conductive to decision making, turning the simulators into essential tools to support the necessary decisions on major investment environments.Simulation tools applied to EM are designed to help market entities to respond efficiently to market unpredictability. Each one of the surrounding entities may present different behaviours resulting from the context and its goals, having an immediate impact on the results obtained in the market [4]. Therefore, simulators allow to perform a stu...

One of the most challenging tasks for energy domain stakeholders is to have a better preview of the electricity consumption. Having a more trustable expectation of electricity consumption can help minimizing the cost of electricity and also enable a better control on the electricity tariff. This paper presents a study using a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) methodology in order to have a better profile of the electricity consumption of the following hours. The proposed approach uses the electricity consumption of the past hours to forecast the consumption value for the following hours. Results from this study are compared to those of previous approaches, namely two fuzzy based systems: and several different approaches based on artificial neural networks. The comparison of the achieved results with those achieved by the previous approaches shows that this approach can calculate a more reliable value for the electricity consumption in the following hours, as it is able to achieve lower forecasting errors, and a less standard deviation of the forecasting error results.

IntroductionWorldwide electricity markets (EM) have undertaken a great revolution with the emergence of the liberalized market [1]. The restructuring of the constituent sectors of EM (production, marketing, transportation and distribution) turned EM into a more competitive environment, which in turn led to increased decision-making difficulty. In order to overcome the complexity and unpredictability of this sector, simulation tools began to be a great investment area. EM simulators are tools that help clarifying the functioning of markets in order to create profiles of the participant players through the analysis, study and forecast of different scenarios [2]. The profiles of participant players allow the understanding of the type of strategies taken by them and support their decision-making processes. The emergence of simulation platforms has been a huge added value since it revolutionized the understanding of the markets operation. Additionally, simulation platforms allow the testing of different participants' behaviors as well as new rules and operations of regulators and market participants [3][4][5].However, simulators of multi-agent energy markets face some problems when it comes to apply the simulated results in reality. In order to create realistic scenarios it is necessary to consider all the real information about the characteristics and behaviors of the players, as well as markets' specifications. This information is available on web platforms of market operators (e.g. MIBEL market data is available in [6]); however, it is not easily interpreted or extracted, which brings many limitations to the process of generating realistic scenarios. Once this limitation is surpassed and the necessary information is obtained, the main problem becomes the treatment of this information in order to create simulations of realistic scenarios.In order to overcome the difficulty that EM simulators face to create realistic scenarios, the Realistic Scenarios Generator (RealScen) has been developed [7]. RealScen uses real data gathered from diverse sources to define the amount, characteristics and behavior of the software agents used in EM simulations, depending on the simulation requirements and on each EM specifications.

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