Machine Learning methods for long and short term energy demand forecasting

The thesis addresses the problems of long- and short- term electric load demand forecasting by using a mixed approach consisting of statistics and machine learning algorithms. The modelling of the multi-seasonal component of the Italian electric load is investigated by spectral analysis combined with machine learning. In particular, a frequency-domain version of the LASSO is developed in order to enforce sparsity in the parameter and efficiently obtain the main harmonics of the multi-seasonal term. The corresponding model yields one-year ahead forecasts whose Mean Absolute Percentage Error (MAPE) has the same order of magnitude of the one-day ahead predictor currently used by the Italian Transmission System Operator. Again for the Italian case, two whole-day ahead predictors are designed. The former applies to normal days while the latter is specifically designed for the Easter week. Concerning normal days, a predictor is built that relies exclusively on the loads recorded in the previous days, without resorting to exogenous data such as weather forecasts. This approach is viable in view of the highly correlated nature of the demand series, provided that suitable regularization-based strategies are applied in order to reduce the degrees of freedom and hence the parameters variance. The obtained forecasts improve significantly on the Terna benchmark predictor. The Easter week predictor is based on a Gaussian process model, whose kernel, differently from standard choices, is statistically designed from historical data. Again, even without using temperatures, a definite improvement is achieved over the Terna predictions. In the last chapter of the thesis, aggregation and enhancement techniques are introduced in order to suitably combine the prediction of different experts. The results, obtained on German national load data, show that, even in the case of missing experts, the proposed strategies yield to more accurate and robust predictions.The thesis addresses the problems of long- and short- term electric load demand forecasting by using a mixed approach consisting of statistics and machine learning algorithms. The modelling of the multi-seasonal component of the Italian electric load is investigated by spectral analysis combined with machine learning. In particular, a frequency-domain version of the LASSO is developed in order to enforce sparsity in the parameter and efficiently obtain the main harmonics of the multi-seasonal term. The corresponding model yields one-year ahead forecasts whose Mean Absolute Percentage Error (MAPE) has the same order of magnitude of the one-day ahead predictor currently used by the Italian Transmission System Operator. Again for the Italian case, two whole-day ahead predictors are designed. The former applies to normal days while the latter is specifically designed for the Easter week. Concerning normal days, a predictor is built that relies exclusively on the loads recorded in the previous days, without resorting to exogenous data such as weather forecasts. This approach is viable in view of the highly correlated nature of the demand series, provided that suitable regularization-based strategies are applied in order to reduce the degrees of freedom and hence the parameters variance. The obtained forecasts improve significantly on the Terna benchmark predictor. The Easter week predictor is based on a Gaussian process model, whose kernel, differently from standard choices, is statistically designed from historical data. Again, even without using temperatures, a definite improvement is achieved over the Terna predictions. In the last chapter of the thesis, aggregation and enhancement techniques are introduced in order to suitably combine the prediction of different experts. The results, obtained on German national load data, show that, even in the case of missing experts, the proposed strategies yield to more accurate and robust predictions

Study on adaptive control of nonlinear dynamical systems based on quansi-ARX models

制度:新 ; 報告番号:甲3441号 ; 学位の種類:博士(工学) ; 授与年月日:15-Sep-11 ; 早大学位記番号:新576

Probabilistic data-driven methods for forecasting, identification and control

This dissertation presents contributions mainly in three different fields: system identification, probabilistic forecasting and stochastic control. Thanks to the concept of dissimilarity and by defining an appropriate dissimilarity function, it is shown that a family of predictors can be obtained. First, a predictor to compute nominal forecastings of a time-series or a dynamical system is presented. The effectiveness of the predictor is shown by means of a numerical example, where daily predictions of a stock index are computed. The obtained results turn out to be better than those obtained with popular machine learning techniques like Neural Networks. Similarly, the aforementioned dissimilarity function can be used to compute conditioned probability distributions. By means of the obtained distributions, interval predictions can be made by using the concept of quantiles. However, in order to do that, it is necessary to integrate the distribution for all the possible values of the output. As this numerical integration process is computationally expensive, an alternate method bypassing the computation of the probability distribution is also proposed. Not only is computationally cheaper but it also allows to compute prediction regions, which are the multivariate version of the interval predictions. Both methods present better results than other baseline approaches in a set of examples, including a stock forecasting example and the prediction of the Lorenz attractor. Furthermore, new methods to obtain models of nonlinear systems by means of input-output data are proposed. Two different model approaches are presented: a local data approach and a kernel-based approach. A kalman filter can be added to improve the quality of the predictions. It is shown that the forecasting performance of the proposed models is better than other machine learning methods in several examples, such as the forecasting of the sunspot number and the R¨ossler attractor. Also, as these models are suitable for Model Predictive Control (MPC), new MPC formulations are proposed. Thanks to the distinctive features of the proposed models, the nonlinear MPC problem can be posed as a simple quadratic programming problem. Finally, by means of a simulation example and a real experiment, it is shown that the controller performs adequately. On the other hand, in the field of stochastic control, several methods to bound the constraint violation rate of any controller under the presence of bounded or unbounded disturbances are presented. These can be used, for example, to tune some hyperparameters of the controller. Some simulation examples are proposed in order to show the functioning of the algorithms. One of these examples considers the management of a data center. Here, an energy-efficient MPC-inspired policy is developed in order to reduce the electricity consumption while keeping the quality of service at acceptable levels

Application of non-linear system identification approaches to modelling, analysis, and control of fluid flows.

Flow control has become a topic of great importance for several applications, ranging from commercial aircraft, to intercontinental pipes and skyscrapers. In these applications, and many more, the interaction with a fluid flow can have a significant influence on the performance of the system. In many cases the fluids encountered are turbulent and detrimental to the latter. Several attempts have been made to solve this problem. However, due to the non-linearity and infinite dimensionality of fluid flows and their governing equations, a complete understanding of turbulent behaviour and a feasible control approach has not been obtained. In this thesis, model reduction approaches that exploit non-linear system identification are applied using data obtained from numerical simulations of turbulent three-dimensional channel flow, and two-dimensional flow over the backward facing step. A multiple-input multiple-output model, consisting of 27 sub-structures, is obtained for the fluctuations of the velocity components of the channel flow. A single-input single-output model for fluctuations of the pressure coefficient, and two multiple-input single-output models for fluctuations of the velocity magnitude are obtained in flow over the BFS. A non-linear model predictive control strategy is designed using identified one- and multi-step ahead predictors, with the inclusion of integral action for robustness. The proposed control approach incorporates a non-linear model without the need for expensive non-linear optimizations. Finally, a frequency domain analysis of unmanipulated turbulent flow is perfumed using five systems. Higher order generalized frequency response functions (GFRF) are computed to study the non-linear energy transfer phenomena. A more detailed investigation is performed using the output FRF (OFRF), which can elucidate the contribution of the n-th order frequency response to the output frequency response

Proceedings. 26. Workshop Computational Intelligence, Dortmund, 24. - 25. November 2016

Dieser Tagungsband enthält die Beiträge des 26. Workshops Computational Intelligence. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen