Hybrid forecast and control chain for operation of flexibility assets in micro-grids

Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are very complex. In this paper, the computational components (such as photovoltaic and load forecasting, and resource scheduling and optimization) are brought together into a practical implementation, introducing an automated system through a chain of independent services aiming to allow forecasting, optimization, and control. Encountered challenges may provide a valuable indication to make ground with this design, especially in cases for which the trade-off between sophistication and available resources should be rather considered. The research work was conducted to identify the requirements for controlling a set of flexibility assets—namely, electrochemical battery storage system and electric car charging station—for a semicommercial use-case by minimizing the operational energy costs for the microgrid considering static and dynamic parameters of the assets

Previsão de demanda de energia elétrica de curto prazo utilizando abordagens de comitês de Wavenets

Orientador : Prof. Dr. Leandro dos Santos CoelhoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 12/04/2017Inclui referências : f. 87-98Área de concentração: Sistemas eletrônicosResumo: A energia elétrica faz parte de um mercado que envolve agentes de geração, transmissão, distribuição e consumo que desejam maximizar seus lucros e minimizar suas despesas. Para isso precisam de um planejamento que tenha como base uma previsão de demanda precisa, já que um cenário pessimista pode levar ao despacho de mais geradores do que o necessário, reserva excessiva de matéria prima e aumento do custo de operação, e por outro lado um cenário otimista pode colocar o sistema elétrico em risco ou exigir a compra de energia no mercado livre a um preço alto. Por isso, a previsão de demanda tem sido empregada em áreas como o agendamento ótimo de geradores, planejamento da manutenção, planejamento da reserva hídrica, compreensão do padrão de consumo, planejamento da expansão e previsão de preços e ajuste de tarifas. Contudo, uma série de demanda é uma série temporal que possui não linearidades e componentes periódicos aleatórios como o clima, perfil dos usuários, eventos públicos, economia, medições erradas, e, consequentemente, um modelo de previsão linear pode não ser apropriado. Este trabalho utiliza diferentes abordagens para formar comitês de wavenets para a previsão de séries temporais de demanda de energia elétrica de curto prazo, os desempenhos são comparados com uma rede neural artificial perceptron multicamadas com função de ativação sigmoide na camada oculta, uma wavenet simples, com a média da última semana e com o modelo inocente. As séries de demanda adotadas, isto é, duas séries de demanda anuais reais com medições horárias, passam por um estágio de pré-processamento para remoção da tendência e normalização, e também para transformação dos valores da série em conjuntos de entrada e saída para o treinamento supervisionado. Emprega-se a estratégia de previsão um passo à frente e a avaliação das previsões é realizada pelo coeficiente de correlação múltipla ???? e também pela análise de correlação entre os resíduos. Para criação dos comitês utiliza-se a reamostragem com reposição, a validação cruzada e a dizimação de entradas, seleção construtiva, combinação pela média simples, moda, mediana e generalização empilhada. Os resultados dos testes de não linearidade demonstram que as duas séries consideradas são não lineares, e também constata-se a diminuição da assimetria dos dados após sua transformação. Do processo de seleção de variáveis obtém-se os atrasos máximos para cada série, valores passados que são utilizados como entradas, e percebe-se que são diferentes para cada série. O atraso máximo a ser utilizado como entrada tem influência na quantidade de amostras do conjunto de dados de entrada e saída. Uma característica dos resultados que se reflete em ambas as séries é o aumento do erro à medida que o horizonte de previsão aumenta. Os comitês de wavenets superam os demais modelos comparados, e, além do desempenho ser diferente para cada problema, o melhor método de aprendizado de comitê a ser utilizado também varia, bem como o horizonte de previsão máximo no qual os valores previstos se ajustam aos valores reais das séries. A qualidade das previsões é avaliada com testes de correlação dos resíduos. Palavras-chave: Wavenet. Previsão de demanda de energia elétrica. Comitês. Redes neurais artificiais.Abstract: Electricity is part of a market which involves generation, transmission, distribution and consumption agents that aim their profit maximization and expenses minimization. To achieve that, they need a planning based on an accurate load forecast, since a pessimistic scenario may lead to more generators dispatch than needed, excessive reservoir and high operating costs, and, on the other hand, an optimistic scenario may place the electrical system at risk or requiring demand electricity purchasing on the free market for a very high price. Hence, load forecasting has been employed in areas such as optimal dispatch, maintenance planning, hydric reservoir planning, consumption pattern understanding, expansion planning, price forecasting and tax adjustments. However, a load series is a time series with nonlinearities and random periodic components as the weather, users profile, public events, economy and bad measures, therefore a pure linear model may not be appropriated. This work uses different approaches to create wavenet ensembles for short term load forecasting, the performances are compared with a multilayer perceptron with sigmoid activation function in the hidden layer, with a single wavenet, with the last week mean and also with the naive model. The load series adopted, that is, two annual hourly load series with actual measurements, are passed through a data pre-processing stage for trend removal and normalization, and also for conversion from the time series to a inputs and output set for supervised training. It is applied the one step ahead forecast strategy and the forecasting evaluation is accomplished by the multiple correlation coefficient, ????, and also by the residuals correlation analysis. For the ensemble creation are used the bootstrapping, cross-validation like, inputs decimation, constructive selection, simple average, median, mode and stacked generalization methods. The nonlinearity tests results demonstrate that both time series are nonlinear, and the asymmetry reduction after data transformation is verified. From the features selection process the maximum lags for each series are identified, lagged values to be used as inputs and it is noticed that they are different for each series. The maximum lag also influences the amount of samples in the dataset of inputs and outputs. A common characteristic of both series is that the error increase along with the prediction horizon. Results point out that the wavenets ensembles overcome the other compared models after tests with two actual annual hourly load series. Moreover, beyond the performance to be different for each problem, the best ensemble learning method also varies, as well as the maximum forecasting horizon for which the forecasted values fit the series actual values. The quality of the forecasts is analyzed through residuals correlation tests. Key-words: Wavenet. Load forecasting. Ensembles. Artificial neural network

User Behavior Clustering Based Method for EV Charging Forecast

The increasing adoption of electric vehicles poses new problems for the electrical distribution network. For this reason, proper electric vehicle forecasting will be of fundamental importance for a predictive energy management system, which could greatly help the operation of the grid. This paper proposes a comprehensive novel methodology to forecast single charging sessions of electric vehicle and the resulting cumulative energy forecast of the charging infrastructure. Historical charging sessions are first clustered on the basis of similar user characteristics and their respective probability density functions are defined. From this, every charging session is predicted with a triplet of parameters, namely the arrival time, the charging duration and the average power expected during the process. The proposed method has been evaluated by considering a real case study. The results showed the ability to greatly improve the accuracy with respect to the chosen benchmark, both in terms of energy required by the station and the predicted number of charging sessions. The overall performance measured by Skill Score is 0.37 for the year 2019

A Temporal Neural Network Model for Probabilistic Multi-Period Forecasting of Distributed Energy Resources

Probabilistic forecasts of electrical loads and photovoltaic generation provide a family of methods able to incorporate uncertainty estimations in predictions. This paper aims to extend the literature on these methods by proposing a novel deep-learning model based on a mixture of convolutional neural networks, transformer models and dynamic Bayesian networks. Further, the paper also illustrates how to utilize Stochastic Variational Inference for training output distributions that allow time series sampling, a possibility not given for most state-of-the-art methods which do not use distributions. On top of this, the model also proposes an encoder-decoder topology that uses matrix transposes in order to both train on the sequential and the feature dimension. The performance of the work is illustrated on both load and generation time series obtained from a site representative of distributed energy resources in Norway and compared to state-of-the-art methods such as long-short-term memory. With a single-minute prediction resolution and a single-second computation time for an update with a batch size of 100 and a horizon of 24 hours, the model promises performance capable of real-time application. In summary, this paper provides a novel model that allows generating future scenarios for time series of distributed energy resources in real-time, which can be used to generate profiles for control problems under uncertainty. INDEX TERMS deep learning, generation forecasting, load forecasting, neural networks, probabilistic methods, renewable powerpublishedVersio

Forecasting Stock Market Indices Using Gated Recurrent Unit (GRU) Based Ensemble Models: LSTM-GRU

A time sequence analysis is a particular method for looking at a group of data points gathered over a long period of time. Instead of merely randomly or infrequently, time series analyzers gather information from data points over a predetermined length of time at scheduled times. But this kind of research requires more than just accumulating data over time. Data in time series may be analyzed to illustrate how variables change over time, which makes them different from other types of data. To put it another way, time is a crucial element since it demonstrates how the data changes over the period of the information and the outcomes. It offers a predetermined architecture of data dependencies as well as an extra data source. Time Series forecasting is a crucial field in deep learning because many forecasting issues have a temporal component. A time series is a collection of observations that are made sequentially across time. In this study, we examine distinct machine learning, deep learning and ensemble model algorithms to predict Nike stock price. We are going to use the Nike stock price data from January 2006 to January 2018 and make predictions accordingly. The outcome demonstrates that the hybrid LSTM-GRU model outperformed the other models in terms of performance

An Experimental Review on Deep Learning Architectures for Time Series Forecasting

In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting; and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50000 time series divided into 12 different forecasting problems. By training more than 38000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient

An Experimental Review on Deep Learning Architectures for Time Series Forecasting

In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50,000 time series divided into 12 different forecasting problems. By training more than 38,000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient.Ministerio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-1263341Junta de Andalucía P18-RT-277