Improving building energy efficiency through novel hybrid models and control approaches including a data center case study
| dc.contributor | Aalto-yliopisto | fi |
| dc.contributor | Aalto University | en |
| dc.contributor.advisor | Lü, Xiaoshu, Prof., Aalto University, Department of Civil Engineering, Finland | |
| dc.contributor.author | Lu, Tao | |
| dc.contributor.department | Rakennustekniikan laitos | fi |
| dc.contributor.department | Department of Civil Engineering | en |
| dc.contributor.school | Insinööritieteiden korkeakoulu | fi |
| dc.contributor.school | School of Engineering | en |
| dc.contributor.supervisor | Puttonen, Jari, Prof., Aalto University, Department of Civil Engineering, Finland | |
| dc.date.accessioned | 2016-09-29T09:01:26Z | |
| dc.date.available | 2016-09-29T09:01:26Z | |
| dc.date.defence | 2016-10-07 | |
| dc.date.issued | 2016 | |
| dc.description.abstract | The building sector consumes the most energy and emits the greatest quantity of greenhouse gases of any sector. Energy savings in this sector can make a major contribution to tackling the threat of climate change. Research has produced a variety of solutions, for example, net zero and positive-energy buildings. At the same time, both models and controls are being challenged by increasingly complex buildings equipped with advanced information and communications technologies (ICT). This dissertation addresses these challenges by proposing a multidisciplinary, wide-ranging modeling methodology that enables new strategies for saving building energy. The core methodology utilizes novel modeling approaches to improve predictive models and produce innovative energy solutions. Models are validated and investigated using a variety of buildings and controls. Data centers and demand controlled ventilation (DCV) are the focus because they represent both "multifunctional buildings" and general energy system controls. This dissertation makes the following seven original contributions: (1) The first systematic, complete case study of a data center in which infrastructure, energy and air management performance, and waste heat recovery systems were investigated, analyzed, and quantified using long-term power consumption data. (2) A novel and tuning-free DCV building control strategy that is far superior to proportional control and more competitive than proportional-integral-derivative (PID) control. (3) An artificial neural network (ANN) model for predicting the water evaporation rate in a swimming hall. (4) A new ANN model for estimating prediction intervals and accounts for the uncertainty of point estimation for indoor conditions in an office building. (5) A new Maximum Likelihood Estimation (MLE) model for predicting constant and time-varying air change rates and a coupled model for estimating the number of occupants in an office. (6) Discovery of a new physical law for run-around heat recovery systems that can be used to develop a simulation model to estimate the system performance for constant volume air (CAV) and DCV systems. This new law was verified in different sites. (7) A new hybrid numerical-ANN model for building performance simulation. The hybrid model can improve not only the model accuracy but also the generalizability of ANN. The results demonstrate the applicability of the modeling techniques and the models, and significant energy savings in buildings. The resulting improvements in model accuracy, forecasting capability, and energy efficiency were published in eight journals. By unifying the results of eight publications, this dissertation presents a comprehensive and coherent study that advances the state-of-the-art building energy research. | en |
| dc.format.extent | 85 + app. 105 | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.isbn | 978-952-60-7008-7 (electronic) | |
| dc.identifier.isbn | 978-952-60-7009-4 (printed) | |
| dc.identifier.issn | 1799-4942 (electronic) | |
| dc.identifier.issn | 1799-4934 (printed) | |
| dc.identifier.issn | 1799-4934 (ISSN-L) | |
| dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/22532 | |
| dc.identifier.urn | URN:ISBN:978-952-60-7008-7 | |
| dc.language.iso | en | en |
| dc.opn | Huai, Xiulan, Prof., Institute of Engineering Thermophysics, Chinese Academy of Science, P.R.China | |
| dc.publisher | Aalto University | en |
| dc.publisher | Aalto-yliopisto | fi |
| dc.relation.haspart | [Publication 1]: Lu, T., Lü, X., Remes, M. & Viljanen, M. (2011). Investigation of air management and energy performance in a data center in Finland: Case study. Energy and Buildings, 43(12), 3360–3372. DOI: 10.1016/j.enbuild.2011.08.034 | |
| dc.relation.haspart | [Publication 2]: Lu, T., Lü, X. & Viljanen, M. (2014). Prediction of water evaporation rate for indoor swimming hall using neural networks. Energy and Buildings 81, 268-280. DOI: 10.1016/j.enbuild.2014.06.027 | |
| dc.relation.haspart | [Publication 3]: Lu, T., Lü, X. & Viljanen, M. (2011). A novel and dynamic demand-controlled ventilation strategy for CO2 control and energy saving in buildings. Energy and Buildings 43(9), 2499-2508. DOI: 10.1016/j.enbuild.2011.06.005 | |
| dc.relation.haspart | [Publication 4]: Lü, X., Lu, T., Viljanen, M. & Kibert, C. (2013). A new method for controlling CO2 in buildings with unscheduled opening hours. Energy and Buildings 59, 161-170. DOI: 10.1016/j.enbuild.2012.12.024 | |
| dc.relation.haspart | [Publication 5]: Lu, T., Lü, X. & Kibert, C. (2015). A hybrid numerical-neural-network model for building simulation: A case study for the simulation of unheated and uncooled indoor temperature. Energy and Buildings 86, 723-734. DOI: 10.1016/j.enbuild.2014.10.024 | |
| dc.relation.haspart | [Publication 6]: Lu, T. & Viljanen, M. (2009). Prediction of indoor temperature and relative humidity using neural network models: model comparison. Neural Computing and Applications 18(4), 345-357. DOI: 10.1007/s00521-008-0185-3 | |
| dc.relation.haspart | [Publication 7]: Lu, T., Knuutila, A., Viljanen, M. & Lü, X. (2010). A novel methodology for estimating space air change rates and occupant CO2 generation rates from measurements in mechanically-ventilated buildings. Building and Environment 45(5), 1161–1172. DOI: http://dx.doi.org/10.1016/j.buildenv.2009.10.024 | |
| dc.relation.haspart | [Publication 8]: Lu, T., Lü, X., Kibert, C. & Puttonen, J. (2016). The application of linear regression and the power law relationship of air-side heat transfer with field measurements to model the performance of run-around heat recovery systems. Energy and Buildings 110, 453-467. DOI: 10.1016/j.enbuild.2015.10.028 | |
| dc.relation.ispartofseries | Aalto University publication series DOCTORAL DISSERTATIONS | en |
| dc.relation.ispartofseries | 182/2016 | |
| dc.rev | Li, Shao, Prof., University of Reading, UK | |
| dc.rev | Simonson, Carey, Prof., University of Saskatchewan, Canada | |
| dc.subject.keyword | neural networks | en |
| dc.subject.keyword | demand-controlled ventilation | en |
| dc.subject.keyword | data center | en |
| dc.subject.keyword | building simulation | en |
| dc.subject.other | Civil engineering | en |
| dc.subject.other | Energy | en |
| dc.title | Improving building energy efficiency through novel hybrid models and control approaches including a data center case study | en |
| dc.type | G5 Artikkeliväitöskirja | fi |
| dc.type.dcmitype | text | en |
| dc.type.ontasot | Doctoral dissertation (article-based) | en |
| dc.type.ontasot | Väitöskirja (artikkeli) | fi |
| local.aalto.archive | yes | |
| local.aalto.formfolder | 2016_09_28_klo_13_28 |
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