No abstract

We propose a method for detecting and forecasting events of high energy demand, which are managed at the national level in demand side response programmes, such as the UK Triads. The methodology consists of two stages: load forecasting with long short-term memory neural network and dynamic filtering of the potential highest electricity demand peaks by using the exponential moving average. The methodology is validated on real data of a UK building management system case study. We demonstrate successful forecasts of Triad events with RRMSE ≈ 2.2% and MAP E ≈ 1.6% and general applicability

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.

No abstract

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.