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Identifying the incidence of free-ridership is significant to a range of issues relevant to program evaluation, including the calculation of net program benefits and more general assessments of political acceptability. Estimates of free-ridership in the area of energy policy frequently rely on ex-post surveys that ask program participants whether they would have behaved differently in the absence of program support. The present paper proposes an ex-ante approach to the calculation of the free-rider share using revealed preference data on home renovations from Germany's residential sector. We employ a discrete-choice model to simulate the effect of grants on renovation choices, the output from which is used to assess the extent of free-ridership under a contemporary subsidy program. Aside from its simplicity, a key advantage of the approach is that it bestows policymakers with an estimate of free-ridership prior to program implementation.

Abstract This paper presents a new approach to heat exchanger network (HEN) design making extensive use of randomization techniques. It is exceedingly simple to implement and gives new insight into the hardness and the cost landscape underlying a given problem. At the same time, the results from our algorithm may be used as good initial solutions required by most non-linear optimization problem formulations of HEN design. Practical networks involve trade-off between a number of factors, all of which are difficult to incorporate in a single design methodology. Our approach is blind to any design heuristic and generates a sufficiently large number of networks that can be further evaluated to pick up the most suitable network depending on specific design requirements. However, the current version of the algorithm is limited to HEN synthesis problems that can be solved without stream splitting. We have experimented with the three standard literature problems and obtained results that compare well with the previously published results, which justify further research in this direction.

This paper describes a hierarchy of increasingly complex statistical models for wind power generation in Alberta applied to wind power production data that are publicly available. The models are based on combining spatial and temporal correlations. We apply the method of Gaussian random fields to analyze the wind power time series of the 19 existing wind farms in Alberta. Following the work of Gneiting et al., three space-time models are used: Stationary, Separability, and Full Symmetry. We build several spatio-temporal covariance function estimates with increasing complexity: separable, non-separable and symmetric, and non-separable and non-symmetric. We compare the performance of the models using kriging predictions and prediction intervals for both the existing wind farms and a new farm in Alberta. It is shown that the spatial correlation in the models captures the predominantly westerly prevailing wind direction. We use the selected model to forecast the mean and the standard deviation of the future aggregate wind power generation of Alberta and investigate new wind farm siting on the basis of reducing aggregate variability.

Parking choice models proposed over recent years have been aimed at choosing the relevant variables and pricing policies affecting said choice. In our analyses, we have encountered some gaps to cover; for this reason, we introduce a methodology for addressing user parking choices, including the availability of parking alternatives in each of the studied zones presenting pivoted scenarios. This methodology has been developed based on a stated preferences survey design to define user behaviour when making parking choices through discrete-choice models. Furthermore, the estimated models have considered systematic and random variations in user tastes and the correlation between similar or related parking alternatives. This methodology has made it possible to provide greater realism in generating fictitious parking scenarios. The results show the importance of the fee attribute and the differences in the users’ preferences depending on whether they live inside or outside the study area and their arrival time at the destination. Several scenarios have been simulated, and in these scenarios, it is found that removing free parking areas or restricting free parking in favour of parking fee regulation in congested zones can be an efficient policy measure to promote other parking alternatives and reduce the number of vehicles searching for a parking space.

The paper presents an original method underlying an efficient tool for assessing the condition of photovoltaic (PV) modules, in particular, those made of amorphous cells. Significantly random changes in operational parameters characterize amorphous cell operation and cause them to be challenging to test, especially in working conditions. To develop the method, the authors modified the residual method with incorporated histograms. The proposed method has been verified through experiments that show the usefulness of the proposed approach. It significantly minimizes the risk of false diagnostic information in assessing the condition of photovoltaic modules. Based on the proposed methods, the inference results confirm the effectiveness of the concept for evaluating the degree of failure of the photovoltaic module described in the paper.

Faults in Heating, Ventilation and Air Conditioning (HVAC) systems affect the energy efficiency of buildings. To date, there rarely exist methods to detect and diagnose faults during the operation of buildings that are both cost-effective and sufficient accurate. This study presents a method that uses artificial intelligence to automate the detection of faults in HVAC systems. The automated fault detection is based on a residual analysis of the predicted total heating power and the actual total heating power using an algorithm that aims to find an optimal decision rule for the determination of faults. The data for this study was provided by a detailed simulation of a residential case study house. A machine learning model and an ARX model predict the building operation. The model for fault detection is trained on a fault-free data set and then tested with a faulty operation. The algorithm for an optimal decision rule uses various statistical tests of residual properties such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that it is possible to predict faults for both known faults and unknown faults. The challenge is to find the optimal algorithm to determine the best decision rules. In the outlook of this study, further methods are presented that aim to solve this challenge.

Abstract Understanding regional building energy patterns is the prerequisite to efficiently and effectively promote sustainable urban development. Previous studies have proposed various data-driven methods to investigate the relationship between building energy consumption and hundreds of potential influencing features. However, it is difficult to include all potential features in one single model since either some data could be unavailable or the model would be too complex. To identify the critical features, this study develops a data-driven random forest (RF) based framework with a dataset of Taipei City, consisting of 24,764 buildings in 881 city-blocks, to model the relationship between city-block-level building-oriented features and building energy consumption. The RF model is found to outperform other machine learning models including logistic regression, k-nearest neighborhood, support vector machine, and decision tree models in the predictive accuracy of the classification problem. Seven critical features related to the built year of buildings, building gross floor area, building density, and the ratio of commercial buildings in the block are identified from the 59 city-block-level building-oriented features. The developed framework could refine the features adopted in regional building energy models, and policymakers and city planners would get practical implications from the identified critical features.

In order to study the changing rule of carbon dioxide emissions in China, this paper systematically focused on their current situation, influencing factors, and future trends. Firstly, the current situations of global carbon dioxide emissions and China’s carbon dioxide emissions were presented via a visualization method and their characteristics were analyzed; secondly, the random forest regression model was used to screen the main factors affecting China’s carbon emissions. Considering the different aspects of carbon emissions, 29 influencing factors were determined and 6 main influencing factors were determined according to the results of the random forest regression model. Then, a prediction model for carbon emissions in China was established. The BP neural network model, multi-factor LSTM time series model, and CNN-LSTM model were compared on the test set and all of them passed the test. However, the goodness of fit of the CNN-LSTM model was about 0.01~0.02 higher than the other two models and the MAE and RMSE of the CNN-LSTM model were about 0.01~0.03 lower than those of the other two models. Thus, it was selected to predict China’s carbon dioxide emissions. The predicted results showed that the peak of China’s carbon emissions will be around 2027 and the peak of these emissions will be between 12.9 billion tons and 13.2 billion tons. Overall, the paper puts forward reasonable suggestions for China’s low-carbon development and provides a reference for an adjustment plan of energy structure.