Energy Analysis of a Real Industrial Building: Model Development, Calibration via Genetic Algorithm and Monitored Data, Optimization of Photovoltaic Integration

This study performs the energy analysis of a real industrial building, located near Naples (South Italy). The used approach includes three phases: development of the energy model, model calibration based on monitored data and optimization of photovoltaic (PV) integration. Monitored data provide the monthly overall electricity demands of the facility for different years, while the load factors of industrial devices are not available. Thus, the assessment of hourly and daily trends of electricity demands and internal heat loads is not possible from monitored data. In order to solve such issue, the energy model of the building is developed under EnergyPlus environment, taking account of the existing PV system too. A genetic algorithm is run by coupling EnergyPlus and MATLAB® to properly calibrate the hourly load factors of the devices in order to achieve a good agreement between simulated and monitored values of monthly electricity demands. Finally, the installation of further PV panels is investigated to optimize the photovoltaic integration with a view to cost-effectiveness. The robustness of the optimization process is ensured using the calibrated energy model, which provides reliable hourly values of building electricity demand. Results show that the electricity produced by the additional PV panels is around 160 MWh per year, while the payback period is around 10 years demonstrating the financial viability of PV integration

Machine Learning based Short-term Prediction of Air-conditioning Load through Smart Meter Analytics

The present paper is focused on short-term prediction of air-conditioning (AC) load of residential buildings using the data obtained from a conventional smart meter. The AC load, at each time step, is separated from smart meter’s aggregate consumption through energy disaggregation methodology. The obtained air-conditioning load and the corresponding historical weather data are then employed as input features for the prediction procedure. In the prediction step, different machine learning algorithms, including Artificial Neural Networks, Support Vector Machines, and Random Forests, are used in order to conduct hour-ahead and day-ahead predictions. The predictions obtained using Random Forests have been demonstrated to be the most accurate ones leading to hour-ahead and day-ahead prediction with R2 scores of 87.3% and 83.2%, respectively. The main advantage of the present methodology is separating the AC consumption from the consumptions of other residential appliances, which can then be predicted employing short-term weather forecasts. The other devices’ consumptions are largely dependent upon the occupant’s behaviour and are thus more difficult to predict. Therefore, the harsh alterations in the consumption of AC equipment, due to variations in the weather conditions, can be predicted with a higher accuracy; which in turn enhances the overall load prediction accuracy

Local online kernel ridge regression for forecasting of urban travel times

Accurate and reliable forecasting of traffic variables is one of the primary functions of Intelligent Transportation Systems. Reliable systems that are able to forecast traffic conditions accurately, multiple time steps into the future, are required for advanced traveller information systems. However, traffic forecasting is a difficult task because of the nonlinear and nonstationary properties of traffic series. Traditional linear models are incapable of modelling such properties, and typically perform poorly, particularly when conditions differ from the norm. Machine learning approaches such as artificial neural networks, nonparametric regression and kernel methods (KMs) have often been shown to outperform linear models in the literature. A bottleneck of the latter approach is that the information pertaining to all previous traffic states must be contained within the kernel, but the computational complexity of KMs usually scales cubically with the number of data points in the kernel. In this paper, a novel kernel-based machine learning (ML) algorithm is developed, namely the local online kernel ridge regression (LOKRR) model. Exploiting the observation that traffic data exhibits strong cyclic patterns characterised by rush hour traffic, LOKRR makes use of local kernels with varying parameters that are defined around each time point. This approach has 3 advantages over the standard single kernel approach: (1) It allows parameters to vary by time of day, capturing the time varying distribution of traffic data; (2) It allows smaller kernels to be defined that contain only the relevant traffic patterns, and; (3) It is online, allowing new traffic data to be incorporated as it arrives. The model is applied to the forecasting of travel times on London's road network, and is found to outperform three benchmark models in forecasting up to 1. h ahead. © 2014 The Authors

Bayesian Based Parameter Identification for Building Energy Models

In this research work, a series of sensitivity analyses were performed to validate the proposed Bayesian approach to identify unknown parameters in building energy models. The proposed Bayesian approach mainly consisted of creating a Gaussian process emulator to sample the posterior distribution. Sensitivity case studies were carried out to investigate followings: appropriate sampling numbers, size of Gaussian process, observation noise, continuous/discrete variables situation. Validation on the proposed approach was done with closed loop results (one RC model and two DOE2.2 models) as well as three actual buildings (two commercial buildings and one residential building). The result showed success of identifying unknown parameters by higher occurrences on target values. Moreover, the proposed approach was tested in actual buildings and shown to calibrate the building energy models with unknown parameters still inside. As an application of the proposed Bayesian approach, development of identification of Energy Conservative Measures (ECMs) were carried out. The proposed approach succeeded in identifying the appropriate ECMs with uncertainties in budgets, initial costs, and actual performance of the ECMs. Furthermore, comparison studies between other linear models and traditional Bayesian approach have been carried out to demonstrate the characteristic of the proposed approach to other methods. Also, this study has validated the possibility of utilizing the simplified approach in a future study

Spatio-temporal forecasting of network data

In the digital age, data are collected in unprecedented volumes on a plethora of networks. These data provide opportunities to develop our understanding of network processes by allowing data to drive method, revealing new and often unexpected insights. To date, there has been extensive research into the structure and function of complex networks, but there is scope for improvement in modelling the spatio-temporal evolution of network processes in order to forecast future conditions. This thesis focusses on forecasting using data collected on road networks. Road traffic congestion is a serious and persistent problem in most major cities around the world, and it is the task of researchers and traffic engineers to make use of voluminous traffic data to help alleviate congestion. Recently, spatio-temporal models have been applied to traffic data, showing improvements over time series methods. Although progress has been made, challenges remain. Firstly, most existing methods perform well under typical conditions, but less well under atypical conditions. Secondly, existing spatio-temporal models have been applied to traffic data with high spatial resolution, and there has been little research into how to incorporate spatial information on spatially sparse sensor networks, where the dependency relationships between locations are uncertain. Thirdly, traffic data is characterised by high missing rates, and existing methods are generally poorly equipped to deal with this in a real time setting. In this thesis, a local online kernel ridge regression model is developed that addresses these three issues, with application to forecasting of travel times collected by automatic number plate recognition on London’s road network. The model parameters can vary spatially and temporally, allowing it to better model the time varying characteristics of traffic data, and to deal with abnormal traffic situations. Methods are defined for linking the spatially sparse sensor network to the physical road network, providing an improved representation of the spatial relationship between sensor locations. The incorporation of the spatio-temporal neighbourhood enables the model to forecast effectively under missing data. The proposed model outperforms a range of benchmark models at forecasting under normal conditions, and under various missing data scenarios