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Gaussian processes (GP) regression is a powerful probabilistic tool for modeling nonlinear dynamical systems. The downside of the method is its cubic computational complexity with respect to the training data that can be partially reduced using pseudo-inputs. The dynamics can be represented with an autoregressive model, which simplifies the training to that of the static case. When simulating an autoregressive model, the uncertainty is propagated through a nonlinear function and the simulation cannot be evaluated in closed-form. This paper combines the variational methods of GP approximations with a nonlinear autoregressive model with exogenous inputs (NARX) to form variational GP (VGP-NARX) models. We show how VGP-NARX models, on average, better approximate a full GP-NARX model than more commonly used GP-NARX (FITC) model on 10 chaotic time-series. The modeling capabilities of VGP-NARX models are compared with the existing approaches on two benchmarks for modeling nonlinear dynamical systems. The advantage of general-purpose computing on graphics processing units (GPGPU) for Monte Carlo simulation on large validation data sets is addressed. • Variational approximation of GP-NARX models for dynamic system identification. • Comparison of GP-NARX approximations for modeling chaotic time-series. • Comparison of VGP-NARX models with existing approaches on dynamic benchmark data. • General-purpose computing on graphics processing units for GP-NARX simulation. [ABSTRACT FROM AUTHOR]

The electrification of public transport in cities is expanding, and fully electric buses (EBs) show great promise because of their high energy efficiency. We present the state of the art for electric-bus-related technologies, taking a closer look at the influence of the route's topography and the climate on battery demands. Included is a brief overview of the EB routes in Europe, which reveals a lack of very demanding benchmark routes, where new technologies from the perspective of seamless driving in difficult conditions could be tested. The trial EB routes set up in many cities allow testing under a variety of driving conditions; however, there is still a need for benchmark routes located in challenging environments with some demanding sections. We suggest a new, demanding, hilly benchmark route that provides the means for a technical evaluation of urban-transport electrification in a geographically specific area with particularly challenging driving conditions. • Introducing electric buses requires their extensive testing on demanding benchmark routes. • There is a lack of demanding city routes for testing electric buses. • The challenges of testing electric buses on urban routes are presented in terms of topography and climate conditions. • A challenging, new, hilly benchmark route is proposed. [ABSTRACT FROM AUTHOR]

Abstract: Planning problems can be solved with a large variety of different approaches, and a significant amount of work has been devoted to the automation of planning processes using different kinds of methods. This paper focuses on the use of specific local search algorithms for real-world production planning based on experiments with real-world data, and presents an adapted local search guided by evolutionary metaheuristics. To make algorithms efficient, many specifics need to be considered and included in the problem solving. We demonstrate that the use of specialized local searches can significantly improve the convergence and efficiency of the algorithm. The paper also includes an experimental study of the efficiency of two memetic algorithms, and presents a real-world software implementation for the production planning. [Copyright &y& Elsevier]

Abstract: This paper presents a design approach for improvement of the efficiency of a universal motor, the type of motor that is typically used in home appliances and power tools. The goal of our optimization was to find optimal values of the independent geometrical parameters of the rotor and the stator of the UM with the aim of reducing the motor''s main power losses—they occur in the iron and the copper. Our procedure is based on a genetic algorithm (GA), and by using it we were able to significantly improve the motor''s efficiency. The GA proved to be a simple and efficient search-and-optimization method for solving this day-to-day design problem in industry. It significantly outperformed a conventional design procedure that was used previously. [Copyright &y& Elsevier]

Highlights: [•] We optimize real-world-industrial production scheduling. [•] A memetic multicriteria metaheuristics is applied to the optimization problem. [•] We lay out an efficient presentation of multiobjective results for expert’s support. [•] Proposed visualization of multiobjective results enables flexible decision-making. [ABSTRACT FROM AUTHOR]

Abstract Forecasting the residential natural gas demand for large groups of buildings is extremely important for efficient logistics in the energy sector. In this paper different forecast models for residential natural gas demand of an urban area were implemented and compared. The models forecast gas demand with hourly resolution up to 60 h into the future. The model forecasts are based on past temperatures, forecasted temperatures and time variables, which include markers for holidays and other occasional events. The models were trained and tested on gas-consumption data gathered in the city of Ljubljana, Slovenia. Machine-learning models were considered, such as linear regression, kernel machine and artificial neural network. Additionally, empirical models were developed based on data analysis. Two most accurate models were found to be recurrent neural network and linear regression model. In realistic setting such trained models can be used in conjunction with a weather-forecasting service to generate forecasts for future gas demand. Highlights • Application of machine learning models to the problem of forecasting residential natural gas demand. • Development of empirical models that stem from the analysis of historical data. • Comparison of models considering various criteria for different forecasting horizons. • Linear regression and recurrent neural network are best under different criteria. • Models are usable in conjunction with weather-forecasting service for real world problems. [ABSTRACT FROM AUTHOR]

Sustainable development requires measures to decrease energy consumption and to avoid any harmful impact on the environment. In recent years, a lot of research has been focused on designing energy systems by emulating the natural ones. This can be regarded as a novel way to support the transformation to sustainable energy systems. The initial step in forming energy systems of thus type, by emulating the ecosystem approach, is the development of a model for rendering the current situation. To contribute to the solution of this problem, an ecosystem model combining the analysis, optimisation and simulation modules has been developed as part of this study. The commonly used models for energy systems are good predictors of the general energy dynamics and structures. However, they can be inadequate when it comes to describing or predicting the complex phenomena within energy systems, such as the large number of parameters, the many end-users, the potential of the technologies, the assessment of the various environmental impacts and the variety of resources. With the implementation of the ecosystem model there is further expanding of the knowledge of an advanced, self-organising methodology integrated within the model’s operation by emulating the natural system dynamics. The ecosystem model could become a valuable tool for developing sustainable energy systems, and allowing the development of the most suitable and sustainable energy system based on the local availability of energy sources. [ABSTRACT FROM AUTHOR]