Machine learning for BMS analysis and optimisation

In large buildings, linking heating, cooling or ventilation systems between themselves and to physical spaces is a very time-consuming task that requires highly skilled engineering knowledge, as all these systems are interconnected and they have a certain influence to each other (ventilation systems are often connected to heating and cooling), which often makes task of locating the sources of error or anomalies very time consuming and difficult as they are performed manually. A different approach would be to work out relationships and equipment linkage from time series data provided by the sensors, thus inferring equipment links from which anomalies can be traced back to the source more easily. This paper proposes a data-based solution to obtain equipment relationships based on cross-correlations to relate Air Handling Units (AHUs) to their respective areas of operation. We also propose a methodology, in particular for AHUs, to identify whether or not to trust correlations based on the difference between supply and return temperature. A case study is presented based a large building with 16 AHU systems.Department for Business, Energy and Industrial Strategy of the United Kingdom; College of Engineering, Design and Physical Sciences of Brunel University Londo

Machine learning for text classification in building management systems

Copyright © 2022 The Author(s). In building management systems (BMS), a medium building may have between 200 and 1000 sensor points. Their labels need to be translated into a naming standard so they can be automatically recognised by the BMS platform. The current industrial practices often manually translate these points into labels (this is known as the tagging process), which takes around 8 hours for every 100 points. We introduce an AI-based multi-stage text classification that translates BMS points into formatted BMS labels. After comparing five different techniques for text classification (logistic regression, random forests, XGBoost, multinomial Naive Bayes and linear support vector classification), we demonstrate that XGBoost is the top performer with 90.29% of true positives, and use the prediction confidence to filter out false positives. This approach can be applied in sensors networks in various applications, where manual free-text data pre-processing remains cumbersome.Department for Business, Energy and Industrial Strategy of the United Kingdom; Brunel University Londo

Carbon savings in the UK demand side response programmes

We quantify carbon (CO2) savings in the demand side response (DSR) programmes. We consider Short Term Operating Reserve (STOR), Triad, Fast Reserve and Smart Meter roll-out, with various types of smart interventions involved (using diesel generators, hydro-pumped generation and use of tariffs). We model CO2 emissions in each of the DSR programmes with appropriate configurations and assumptions used in the energy industry. This enables us to compare carbon emissions between the business-as-usual (BAU) solutions and the smart intervention applied, thus deriving the carbon savings. Whether such DSR produces positive CO2 savings or not depends on the used technologies, as well as the scale of the interventions, which we illustrate in examples

Universal features of correlated bursty behaviour

Inhomogeneous temporal processes, like those appearing in human communications, neuron spike trains, and seismic signals, consist of high-activity bursty intervals alternating with long low-activity periods. In recent studies such bursty behavior has been characterized by a fat-tailed inter-event time distribution, while temporal correlations were measured by the autocorrelation function. However, these characteristic functions are not capable to fully characterize temporally correlated heterogenous behavior. Here we show that the distribution of the number of events in a bursty period serves as a good indicator of the dependencies, leading to the universal observation of power-law distribution in a broad class of phenomena. We find that the correlations in these quite different systems can be commonly interpreted by memory effects and described by a simple phenomenological model, which displays temporal behavior qualitatively similar to that in real systems

Early warning signals of simulated Amazon rainforest dieback

Copyright © The Author(s) 2013. This article is published with open access at Springerlink.comWe test proposed generic tipping point early warning signals in a complex climate model (HadCM3) which simulates future dieback of the Amazon rainforest. The equation governing tree cover in the model suggests that zero and non-zero stable states of tree cover co-exist, and a transcritical bifurcation is approached as productivity declines. Forest dieback is a non-linear change in the non-zero tree cover state, as productivity declines, which should exhibit critical slowing down. We use an ensemble of versions of HadCM3 to test for the corresponding early warning signals. However, on approaching simulated Amazon dieback, expected early warning signals of critical slowing down are not seen in tree cover, vegetation carbon or net primary productivity. The lack of a convincing trend in autocorrelation appears to be a result of the system being forced rapidly and non-linearly. There is a robust rise in variance with time, but this can be explained by increases in inter-annual temperature and precipitation variability that force the forest. This failure of generic early warning indicators led us to seek more system-specific, observable indicators of changing forest stability in the model. The sensitivity of net ecosystem productivity to temperature anomalies (a negative correlation) generally increases as dieback approaches, which is attributable to a non-linear sensitivity of ecosystem respiration to temperature. As a result, the sensitivity of atmospheric CO2 anomalies to temperature anomalies (a positive correlation) increases as dieback approaches. This stability indicator has the benefit of being readily observable in the real world.NERCJoint DECC/Defra Met Office Hadley Centre Climate ProgrammeUniversity of Exete

Potential analysis reveals changing number of climate states during the last 60 kyr

We develop and apply a new statistical method of potential analysis for detecting the number of states of a geophysical system, from its recorded time series. Estimation of the degree of a polynomial potential allows us to derive the number of potential wells in a system. The method correctly detects changes in the number of wells in artificial data. In ice-core proxy records of Greenland paleotemperature, a reduction in the number of climate states from two to one is detected sometime prior to the last glacial maximum (LGM), 23-19 kyr BP. This bifurcation can be interpreted as loss of stability of the warm interstadial state of the Dansgaard-Oeschger events. In data spanning the last glacial termination, up to four climate states are detected, plausibly representing the LGM, Bolling-Allerod, Younger Dryas, and the Holocene. The proposed method can be applied to a wide range of geophysical time series exhibiting bifurcations

Long-term memory in earthquakes and the distribution of interoccurrence times

We study seismic records in regimes of stationary seismic activity in Northern and Southern California. Our analysis suggests that the earthquakes are long-term power law correlated with a correlation exponent ? close to 0.4. We show explicitly that the long-term correlations can explain both the fluctuations of magnitudes and interoccurrence times (between events above a certain magnitude M) and, without any fit parameter, the scaling form of the distribution function of the interoccurrence times in the seismic records, recently obtained by Corral (Phys. Rev. Lett., 92 (2004) 108501)

Early Warning Signals of Failures in Building Management Systems

Copyright © J.J. Mesa-Jiménez et al. In the context of sensor data generated by Building Management Systems (BMS), early warning signals are still an unexplored topic. The early detection of anomalies can help preventing malfunctions of key parts of a heating, cooling and air conditioning (HVAC) system that may lead to a range of BMS problems, from important energy waste to fatal errors in the worst case. We analyse early warning signals in BMS sensor data for early failure detection. In this paper, the studied failure is a malfunction of one specific Air Handling Unit (AHU) control system that causes temperature spikes of up to 30 degrees Celsius due to overreaction of the heating and cooling valves in response to an anomalous temperature change caused by the pre-heat coil in winter period in a specific area of a manufacturing facility. For such purpose, variance, lag-1 autocorrelation function (ACF1), power spectrum (PS) and variational autoencoder (VAE) techniques are applied to both univariate and multivariate scenarios. The univariate scenario considers the application of these techniques to the control variable only (the one that displays the failure), whereas the multivariate analysis considers the variables affecting the control variable for the same purpose. Results show that anomalies can be detected up to 32 hours prior to failure, which gives sufficient time to BMS engineers to prevent a failure and therefore, an proactive approach to BMS failures is adopted instead of a reactive one.EPSRC DTP PhD Studentship, NPL and Mitie