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Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated from data collected from a multitude of large-scale public buildings across China. [ABSTRACT FROM AUTHOR]

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle speed (km/h). Instantaneous fuel consumption data can help to predict patterns in fuel consumption for optimized fleet operations. In this work, the data used for modeling was collected at a frequency of 1Hz during on-road testing of modern heavy-duty vehicles (HDV) at the West Virginia University Center for Alternative Fuels Engines and Emissions (WVU CAFEE) using the portable emissions monitoring system (PEMS). The performance of the artificial neural network was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The model was further evaluated with data collected from a vehicle on-road trip. The study shows that artificial neural networks performed slightly better than other machine learning techniques such as linear regression (LR), and random forest (RF), with high R-squared (R2) and lower root mean square error. [ABSTRACT FROM AUTHOR]

Energy efficiency benchmarking can be used to monitor changes in the overall efficiency of buildings. Benchmarking models, based on energy efficiency indicators, are valuable tools for both public and private stakeholders because they allow an improvement in the building energy management. For the last decade, some governments have used these tools to define their building regulations (Santamouris, M., et al., 2005. Energy performance of residential buildings book. UK: James and James/Earthscan. ISBN: 1-902916-49-2, Chung, W., Hui, Y.V., and Miu Lam, Y., 2006. Benchmarking the energy efficiency of commercial buildings. Applied Energy, 83, 1-14). This paper tries to go further, integrating a benchmarking and a modelling process, into the same energy efficiency analysis. The connections between the energy use intensities (EUIs) and the characteristic building factors are modelled using neural network techniques. The process is divided into two stages: the data acquisition stage and the benchmarking and modelling stage. The benchmarking and modelling stage is focused on the adjustment of these EUIs within the climatic conditions (with a severity climate index method) and the development of a prediction model for calculating the relationship between these climate-adjusted EUIs and the significant factors of a building. In order to validate this methodology, an application to schools in Catalonia is presented. Additionally, the use of the artificial neural network (ANN) benchmark model for predicting potential energy savings from retrofit projects was evaluated. Some of the input variables were modified to reflect potential energy savings from a retrofit project, and the new input set was simulated with the ANN model. The preliminary results show that the developed ANN model can be used to predict energy savings from retrofit projects. [ABSTRACT FROM AUTHOR]

Abstract: The use of electricity is indispensable to modern life. As Macao Special Administrative Region becomes a gaming and tourism center in Asia, modeling the consumption of electricity is critical to Macao''s economic development. The purposes of this paper are to conduct an extensive literature review on modeling of electricity consumption, and to identify key climatic, demographic, economic and/or industrial factors that may affect the electricity consumption of a country/city. It was identified that the five factors, namely temperature, population, the number of tourists, hotel room occupancy and days per month, could be used to characterize Macao''s monthly electricity consumption. Three selected approaches including multiple regression, artificial neural network (ANN) and wavelet ANN were used to derive mathematical models of the electricity consumption. The accuracy of these models was assessed by using the mean squared error (MSE), the mean squared percentage error (MSPE) and the mean absolute percentage error (MAPE). The error analysis shows that wavelet ANN has a very promising forecasting capability and can reveal the periodicity of electricity consumption. [Copyright &y& Elsevier]