Electricity Price Forecasting using Asymmetric Fuzzy Neural Network Systems

Electricity price forecasting is considered as an important tool for energy-related utilities and power generation industries. The deregulation of power market, as well as the competitive financial environment, which have introduced new market players in this field, makes the electricity price forecasting problem a demanding mission. The main focus of this paper is to investigate the performance of asymmetric neuro-fuzzy network models for day-ahead electricity price forecasting. The proposed model has been developed from existing Takagi–Sugeno–Kang fuzzy systems by substituting the IF part of fuzzy rules with an asymmetric Gaussian function. In addition, a clustering method is utilised as a pre-processing scheme to identify the initial set and adequate number of clusters and eventually the number of rules in the proposed model. The results corresponding to the minimum and maximum electricity price have indicated that the proposed forecasting scheme could be considered as an improved tool for the forecasting accuracy

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Day ahead hourly Price Forecast in ISO New England Market using Neuro-Fuzzy Systems

Accurate electricity price forecasting is an alarming challenge for market participants and managers owing to high volatility of the electricity prices. Price forecasting is also the most important management goal for market participants since it forms the basis of maximizing profits. These markets are usually organized in power pools and administrated by the independent system operator (ISO). The aim of this study is to examine the performance of asymmetric neuro-fuzzy network models for day-ahead electricity price forecasting in the ISO New England market. The implemented model has been developed with two alternative defuzzification models. The first model follows the Takagi–Sugeno–Kang scheme, while the second the traditional centre of average method. A clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of rules in the network. Simulation results corresponding to the minimum and maximum electricity price indicate that the proposed network architectures could provide a considerable improvement for the forecasting accuracy compared to alternative learning-based scheme

Neuro-Fuzzy Based Intelligent Approaches to Nonlinear System Identification and Forecasting

Nearly three decades back nonlinear system identification consisted of several ad-hoc approaches, which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks and the fuzzy logic combined with optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or unpredictable, ill or well defined. Neurofuzzy hybrid modelling approaches have been developed as an ideal technique for utilising linguistic values and numerical data. This Thesis is focused on the development of advanced neurofuzzy modelling architectures and their application to real case studies. Three potential requirements have been identified as desirable characteristics for such design: A model needs to have minimum number of rules; a model needs to be generic acting either as Multi-Input-Single-Output (MISO) or Multi-Input-Multi-Output (MIMO) identification model; a model needs to have a versatile nonlinear membership function. Initially, a MIMO Adaptive Fuzzy Logic System (AFLS) model which incorporates a prototype defuzzification scheme, while utilising an efficient, compared to the Takagi–Sugeno–Kang (TSK) based systems, fuzzification layer has been developed for the detection of meat spoilage using Fourier transform infrared (FTIR) spectroscopy. The identification strategy involved not only the classification of beef fillet samples in their respective quality class (i.e. fresh, semi-fresh and spoiled), but also the simultaneous prediction of their associated microbiological population directly from FTIR spectra. In the case of AFLS, the number of memberships for each input variable was directly associated to the number of rules, hence, the “curse of dimensionality” problem was significantly reduced. Results confirmed the advantage of the proposed scheme against Adaptive Neurofuzzy Inference System (ANFIS), Multilayer Perceptron (MLP) and Partial Least Squares (PLS) techniques used in the same case study. In the case of MISO systems, the TSK based structure, has been utilized in many neurofuzzy systems, like ANFIS. At the next stage of research, an Adaptive Fuzzy Inference Neural Network (AFINN) has been developed for the monitoring the spoilage of minced beef utilising multispectral imaging information. This model, which follows the TSK structure, incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. In this specific case study, AFINN model was also able to predict for the first time in the literature, the beef’s temperature directly from imaging information. Results again proved the superiority of the adopted model. By extending the line of research and adopting specific design concepts from the previous case studies, the Asymmetric Gaussian Fuzzy Inference Neural Network (AGFINN) architecture has been developed. This architecture has been designed based on the above design principles. A clustering preprocessing scheme has been applied to minimise the number of fuzzy rules. AGFINN incorporates features from the AFLS concept, by having the same number of rules as well as fuzzy memberships. In spite of the extensive use of the standard symmetric Gaussian membership functions, AGFINN utilizes an asymmetric function acting as input linguistic node. Since the asymmetric Gaussian membership function’s variability and flexibility are higher than the traditional one, it can partition the input space more effectively. AGFINN can be built either as an MISO or as an MIMO system. In the MISO case, a TSK defuzzification scheme has been implemented, while two different learning algorithms have been implemented. AGFINN has been tested on real datasets related to electricity price forecasting for the ISO New England Power Distribution System. Its performance was compared against a number of alternative models, including ANFIS, AFLS, MLP and Wavelet Neural Network (WNN), and proved to be superior. The concept of asymmetric functions proved to be a valid hypothesis and certainly it can find application to other architectures, such as in Fuzzy Wavelet Neural Network models, by designing a suitable flexible wavelet membership function. AGFINN’s MIMO characteristics also make the proposed architecture suitable for a larger range of applications/problems

A Multi-Agent System Simulation Model for Trusted Local Energy Markets

The energy market and electric grid play a major role in everyday life. Most areas in modern society, such as: communication, health, transportation, the financial system and many others; require electrical energy to operate properly. Traditionally energy grids operate in a centralized manner. Consumers are connected to centralized utilities in the grid and energy flows from producers to Consumers. However, the rising in popularity in Renewable Energy Sources (RES) such as photovoltaic panels installed in households, small commerce and small industry wide spread the use of distributed energy generation, which the main energy grid was not designed to support. One of the possible solutions for this problem is the creation of a Local Energy Market (LeM). A LeM is a market that operates in a small physical area such as a neighborhood. Traditional consumers can become active market participants under a LeM. That is possible because the LeM is structured in such a way as to enable small-scale negotiations and energy exchanges between participants, who traditionally would only be final consumers. The LeM is capable of dealing with distributed energy generation from RES because negotiations and distribution happen at a local level, thus reducing problems with the main grid. Furthermore, the participation in the local market can reduce energy costs or even create profits for consumers, while contributing to easy the management of the grid and associated technical losses. This work explores the concept of LeM and is focused on two main objectives: designing and developing a system that allows the simulation of LeM, and designing and developing a mechanism that allows trusted negotiations in this market. To accomplish these objectives a Multi-Agent System (MAS) architecture is proposed to model and allow the simulation of LeM. Furthermore to support the market it is also proposed a trust model used to evaluate the behavior of participants and detecting faulty or malicious activities. The developed MAS models a LeM based on a Smart Grid, that is an energy grid with a cyber-physical system with smart meters and communications mechanisms. The MAS was developed with agents to model sensors, market participants and a Market Interaction Manager (MIM) agent that is responsible for managing the negotiations and for applying trust mechanisms. The trust mechanism was designed to attribute a dynamic trust value to each participant, which is reviewed during the all negotiation period. This evaluation of the participant’s trust is based on the analysis of historical data, contextual data, such as weather conditions, and by using forecasting methods to predict the participant expected behavior, allowing to penalize the ones that are exhibiting a questionable behavior in the market. A case study simulation was made with the objective of understanding how the proposed trust mechanism performed, and how the use of different forecasting methods can interfere with it. The results obtained allowed us to conclude that the trust methodology is able to update the trust of each participant, during the negotiation period, and when paired with a well performing forecasting mechanism it is able to achieve a trusted evaluation of the participants behavior. Taking into consideration these results we believe that the proposed trust methodology is capable of providing a valuable trust assessment when used by the MIM agent. This Master Thesis is developed within the scope of a project called Secure interactions and trusted Participation in local Electricity Trading (SPET), a FCT-SAICT2017 funded Research & Development project. SPET project envisions the development of a MAS that is designed to model and simulate the operations of a LeM, taking a focus on security and market trust necessary in this negotiation environment.O mercado de energia e a rede elétrica desempenham um papel importante na vida quotidiana da população. Grande parte das áreas da sociedade moderna, como é o caso da comunicação, transportes, saúde, sistema financeiro, entre outras; requer energia elétrica para funcionar corretamente. Tradicionalmente, as redes de energia operam de forma centralizada. Os consumidores estão conectados a fornecedores centralizados na rede e a energia é transferida dos produtores para os consumidores. No entanto, o aumento da popularidade das Fontes de Energia Renováveis (FER), como painéis fotovoltaicos instalados nas residências, pequeno comércio e pequena indústria, difundiu o uso da geração distribuída de energia, que a rede principal de energia não foi projetada para suportar. Uma das possíveis soluções para esse problema é a criação de um Mercado Local de Energia (MLe). Um MLe é um mercado que opera numa pequena área física, como uma vizinhança. Num MLe, os consumidores tradicionais têm a possibilidade de ser participantes ativos no mercado. Isto é possível porque o MLe está estruturado de forma a permitir negociações em pequena escala e trocas de energia entre os participantes, que tradicionalmente seriam apenas consumidores finais. O MLe é capaz de lidar com a geração de energia distribuída proveniente das FER, porque as negociações e a distribuição ocorrem a um nível local, reduzindo assim os problemas com a rede principal. Para além disso, a participação no mercado local pode reduzir os custos de energia ou até gerar lucros para os consumidores, contribuindo ainda para facilitar a gestão da rede e reduzir as perdas técnicas a ela associadas. Este trabalho explora o conceito de MLe e está focado em dois objetivos principais: projetar e desenvolver um sistema que permita a simulação de MLe, bem como um mecanismo que permita negociações confiáveis neste mercado. Para atingir estes objetivos, é proposta uma arquitetura de Sistema Multi-Agente (SMA) para modelar e permitir a simulação do MLe. Para além disso, para apoiar o mercado, também é proposto um modelo de confiança utilizado para avaliar o comportamento dos participantes e detetar falhas ou atividades maliciosas. O SMA desenvolvido modela um MLe com base numa Smart Grid, que é uma rede de energia com um sistema ciber-físico, com sensores inteligentes e mecanismos de comunicação. O SMA foi desenvolvido com agentes para modelar sensores, participantes do mercado e um agente Market Interaction Manager (MIM), responsável pela gestão das negociações e pela aplicação de mecanismos de confiança. O mecanismo de confiança foi projetado para atribuir um valor de confiança dinâmico a cada participante, que é adaptado durante todo o período de negociação. Essa avaliação da confiança do participante é baseada na análise de dados históricos, contextuais, como condições climatéricas, e no uso de métodos de previsão para antever o comportamento esperado do participante, permitindo penalizar aqueles que exibem um comportamento questionável no mercado. Foi realizada uma simulação de caso de estudo, com o objetivo de avaliar o desempenho do mecanismo de confiança proposto e de que forma é que o uso de diferentes métodos de previsão interfere neste desempenho. Os resultados obtidos permitiram concluir que a metodologia de confiança é capaz de atualizar a confiança de cada participante, durante o período de negociação e, quando combinada com um mecanismo de previsão com bom desempenho, é capaz de obter uma avaliação confiável do comportamento dos participantes. Tendo em consideração estes resultados, acreditamos que a metodologia de confiança proposta é capaz de fornecer uma avaliação de confiança valiosa quando usada pelo agente MIM. Esta tese de mestrado é desenvolvida no âmbito de um projeto chamado Secure interactions and trusted Participation in local Electricity Trading (SPET), um projeto de Investigação e Desenvolvimento (I&D) financiado pela FCT-SAICT2017. O projeto SPET tem como objetivo o desenvolvimento de um MAS para a modelação e simulação de MLe, tendo como foco a segurança e confiança necessárias neste ambiente de negociação

FORECASTING SPOT ELECTRICITY PRICES WITH TIME SERIES MODELS

In this paper we study simple time series models and assess their forecasting performance. In particular we calibrate ARMA and ARMAX (where the exogenous variable is the system load) processes. Models are tested on a time series of California power market system prices and loads from the period proceeding and including the market crash.Electricity, price forecasting, ARMA model, seasonal component

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly

Forecasting Automobile Demand Via Artificial Neural Networks & Neuro-Fuzzy Systems

The objective of this research is to obtain an accurate forecasting model for the demand for automobiles in Iran\u27s domestic market. The model is constructed using production data for vehicles manufactured from 2006 to 2016, by Iranian car makers. The increasing demand for transportation and automobiles in Iran necessitated an accurate forecasting model for car manufacturing companies in Iran so that future demand is met. Demand is deduced as a function of the historical data. The monthly gold, rubber, and iron ore prices along with the monthly commodity metals price index and the Stock index of Iran are Artificial neural network (ANN) and artificial neuro-fuzzy system (ANFIS) have been utilized in many fields such as energy consumption and load forecasting fields. The performances of the methodologies are investigated towards obtaining the most accurate forecasting model in terms of the forecast Mean Absolute Percentage Error (MAPE). It was concluded that the feedforward multi-layer perceptron network with back-propagation and the Levenberg-Marquardt learning algorithm provides forecasts with the lowest MAPE (5.85%) among the other models. Further development of the ANN network based on more data is recommended to enhance the model and obtain more accurate networks and subsequently improved forecasts