State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Small-Scale Compressed Air Energy Storage Application for Renewable Energy Integration in a Listed Building

In the European Union (EU), where architectural heritage is significant, enhancing the energy performance of historical buildings is of great interest. Constraints such as the lack of space, especially within the historical centers and architectural peculiarities, make the application of technologies for renewable energy production and storage a challenging issue. This study presents a prototype system consisting of using the renewable energy from a photovoltaic (PV) array to compress air for a later expansion to produce electricity when needed. The PV-integrated small-scale compressed air energy storage system is designed to address the architectural constraints. It is located in the unoccupied basement of the building. An energy analysis was carried out for assessing the performance of the proposed system. The novelty of this study is to introduce experimental data of a CAES (compressed air energy storage) prototype that is suitable for dwelling applications as well as integration accounting for architectural constraints. The simulation, which was carried out for an average summer day, shows that the compression phase absorbs 32% of the PV energy excess in a vessel of 1.7 m(3), and the expansion phase covers 21.9% of the dwelling energy demand. The electrical efficiency of a daily cycle is equal to 11.6%. If air is compressed at 225 bar instead of 30 bar, 96.0% of PV energy excess is stored in a volume of 0.25 m3, with a production of 1.273 kWh, which is 26.0% of the demand

Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively