Assessing the performance gap of two dynamic thermal modelling software tools when comparing with real-time data in relation to thermal loss

Managing thermal loss is a key topic that needs further investigation as it has a direct link to reducing the energy load in buildings. One of these thermal loss management methods can be the use of shading devices. Dynamic thermal models normally used at the early stages of the building design can play an important role in the decision-making process regarding the use of shading devices. This paper presents the results of a real-world study assessing the potential of using a sealed cellular blind as a passive energy conservation method, where the real-world results are compared with the simulated results generated with environmental design solutions limited thermal analysis software (EDSL Tas) and integrated environmental solutions virtual environment (IES VE). During the real-world study, a positive impact of having blinds was seen whereby the window surface temperature increased and office heating energy consumption was lowered. Both software tools were able to predict a similar trend of results for the window surface temperature in with and without blind scenarios whereas for energy consumption although in the presence of a blind a consistent correlation is seen between measured and calculated values but not without a blind. This can be attributed to the inability of the software tools in demonstrating the effect of in filtration in the absence of a blind or shading device i.e., a clear window scenario.Practical Application: The performance gap analysis regarding thermal loss between dynamic thermal models and real-world settings within buildings can enhance the predictability of the building energy software tools used by designers. Early design inputs within buildings can prevent costly building re-work to improve the building’s energy performance. This can also improve the understanding within the building industry of the importance of reducing thermal loss through the use of shading devices and ensuring the software tools used to model these devices are as close to real-world settings as possible

Comparison of Real-world Data with Simulated Results to Enhance Building Thermal Retention when using Shading Devices

Managing thermal loss is a key topic that needs further investigation as it has a direct link to reducing the energy load in buildings. One of these thermal loss management methods can be the use of shading devices. Dynamic thermal models normally used at the early stages of the building design can play an important role in the decisionmaking process regarding the use of shading devices. This paper presents the results of a real-world study assessing the potential of using a sealed cellular blind as a passive energy conservation method, where the real-world results are compared with the simulated results generated with EDSL Tas. During the real-world study, a positive impact of having blinds was seen whereby the window surface temperature increased and office heating energy consumption was lowered. EDSL Tas was able to predict a similar trend of results for the window surface temperature but not for the energy consumption. This was mainly due to the inability of the software in demonstrating the effect of infiltration of the blind

CARLA: A Self-supervised Contrastive Representation Learning Approach for Time Series Anomaly Detection

We introduce a Self-supervised Contrastive Representation Learning Approach for Time Series Anomaly Detection (CARLA), an innovative end-to-end self-supervised framework carefully developed to identify anomalous patterns in both univariate and multivariate time series data. By taking advantage of contrastive representation learning, We introduce an innovative end-to-end self-supervised deep learning framework carefully developed to identify anomalous patterns in both univariate and multivariate time series data. By taking advantage of contrastive representation learning, CARLA effectively generates robust representations for time series windows. It achieves this by 1) learning similar representations for temporally close windows and dissimilar representations for windows and their equivalent anomalous windows and 2) employing a self-supervised approach to classify normal/anomalous representations of windows based on their nearest/furthest neighbours in the representation space. Most of the existing models focus on learning normal behaviour. The normal boundary is often tightly defined, which can result in slight deviations being classified as anomalies, resulting in a high false positive rate and limited ability to generalise normal patterns. CARLA's contrastive learning methodology promotes the production of highly consistent and discriminative predictions, thereby empowering us to adeptly address the inherent challenges associated with anomaly detection in time series data. Through extensive experimentation on 7 standard real-world time series anomaly detection benchmark datasets, CARLA demonstrates F1 and AU-PR superior to existing state-of-the-art results. Our research highlights the immense potential of contrastive representation learning in advancing the field of time series anomaly detection, thus paving the way for novel applications and in-depth exploration in this domain.Comment: 33 pages, 9 figures, 10 table

Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series

Time series classification (TSC) is a challenging task due to the diversity of types of feature that may be relevant for different classification tasks, including trends, variance, frequency, magnitude, and various patterns. To address this challenge, several alternative classes of approach have been developed, including similarity-based, features and intervals, shapelets, dictionary, kernel, neural network, and hybrid approaches. While kernel, neural network, and hybrid approaches perform well overall, some specialized approaches are better suited for specific tasks. In this paper, we propose a new similarity-based classifier, Proximity Forest version 2.0 (PF 2.0), which outperforms previous state-of-the-art similarity-based classifiers across the UCR benchmark and outperforms state-of-the-art kernel, neural network, and hybrid methods on specific datasets in the benchmark that are best addressed by similarity-base methods. PF 2.0 incorporates three recent advances in time series similarity measures -- (1) computationally efficient early abandoning and pruning to speedup elastic similarity computations; (2) a new elastic similarity measure, Amerced Dynamic Time Warping (ADTW); and (3) cost function tuning. It rationalizes the set of similarity measures employed, reducing the eight base measures of the original PF to three and using the first derivative transform with all similarity measures, rather than a limited subset. We have implemented both PF 1.0 and PF 2.0 in a single C++ framework, making the PF framework more efficient

Improving Position Encoding of Transformers for Multivariate Time Series Classification

Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to capture the ordering of the time series data. The efficacy of position encoding in time series analysis is not well-studied and remains controversial, e.g., whether it is better to inject absolute position encoding or relative position encoding, or a combination of them. In order to clarify this, we first review existing absolute and relative position encoding methods when applied in time series classification. We then proposed a new absolute position encoding method dedicated to time series data called time Absolute Position Encoding (tAPE). Our new method incorporates the series length and input embedding dimension in absolute position encoding. Additionally, we propose computationally Efficient implementation of Relative Position Encoding (eRPE) to improve generalisability for time series. We then propose a novel multivariate time series classification (MTSC) model combining tAPE/eRPE and convolution-based input encoding named ConvTran to improve the position and data embedding of time series data. The proposed absolute and relative position encoding methods are simple and efficient. They can be easily integrated into transformer blocks and used for downstream tasks such as forecasting, extrinsic regression, and anomaly detection. Extensive experiments on 32 multivariate time-series datasets show that our model is significantly more accurate than state-of-the-art convolution and transformer-based models. Code and models are open-sourced at \url{https://github.com/Navidfoumani/ConvTran}

UPOTREBA POLIETILENSKOG GLIKOLA (6000) ZA DEAKTIVIRANJE TANINA U LIŠĆU DRVEĆA ZA BRŠĆENJE U ISTOČNOJ LIBIJI

This study was conducted to investigate the effect of different levels of Polyethylene glycol (PEG) on in vitro gas production of some tree leaves grown in Eastern Libya. The samples used were Ceratonia siliqua, Pistacia lentiscus, and Acacia cyanophylla. They were incubated anaerobically with rumen liquor from growing male sheep equipped with a permanent cannula. Cumulative gas production was measured after 3, 6, 12, 24, 48 and 72 hours from the incubation of samples with rumen liquor. PEG was added at levels 15, 30 and 45 mg per 0.2 g dry matter. The chemical analysis showed that the crude protein (%) was 8.2, 9.5, 13.4, for P. lentiscus, C. siliqua and A. cyanophylla, respectively, and that of the ether extracts were nearly the same in all the studied samples. The NDF contents were ranged between 44.5% for C. siliqua and 37% for A. cyanophylla with P. lentiscus lie in the middle (39.8%). The percentages of condensed tannins were 25.4, 21.5 and 4.1 for A. cyanophylla, P. lentiscus and C.siliqua respectively. The average cumulative gas production (ml/0.2 g DM) after 48h of incubation was higher (P<0.05) for C. siliqua then P. lentiscus followed by A. cyanophylla (22.9, 12.6 and 8.2) respectively. Addition of Polyethylene glycol (15, 30 and 45 mg PEG/0.2 g DM) increased (P<0.05) the cumulative gas production compared with control (13.5, 16.4 and 20 vs. 8.4). The current study concluded that PEG can be used to alleviate the undesirable effects of anti-nutritional Polyphenols found in some grazing tree leaves.Ovo je istraživanje provedeno radi ispitivanja djelovanja raznih razina polietilenskog glikola (PEG) na in vitro proizvodnju plina lišća nekog drveća što raste u Istočnoj Libiji. Upotrijebljeni uzorci bili su Ceratonia siliqua, Pistacia lentiscus i Acacia cyanophylla. Uzorci su inkubirani anaerobno s tekućinom iz buraga muških ovaca u porastu s trajno ugrađenom kanilom. Nakupljeni plin mjeren je nakon 3, 6, 12, 24, 48 i 72 sata od inkubacije uzoraka s tekućinom iz buraga. PEG je dodan u razinama od 15,30 i 45 mg na 0,2 g suhe tvari. Kemijska analiza je pokazala da su vrijednosti sirovih bjelančevina iznosile 8.2, 9.5 i 13.4 za P.lentiscus, C.siliqua odnosno A.cyanophylla, a bjelančevine drugih ekstrakata bile su gotovo iste u svim ispitivanim uzorcima. Sadržaj NDF iznosio je 44,5% za C.siliqua i 37% za A.cyanophylla dok je P.lentiscus bio u sredini (39,8%). Ostatci kondenziranih tanina bili su 25.4, 21.5 i 4,1 za A.cyanophylla, P.lentiscus odnosno C.siliqua. Prosječni nakupljeni plin (ml/0,2 g DM) nakon inkubacije od 48h bio je viši(P<0,05) za C.siliqua zatim slijede P.lentiscus i A.cyanophylla (22.9,12.6 i 8.2).Dodavanje polietilenskog glikola (15,30 i 45 mg PEG/0.2 g DM) povisilo je (P<0,05) ukupnu proizvodnju plina u usporedbi s kontrolom (13.5,16.4 i 20 vs.8.4). Prema ovom istraživaanju PRG se može upotrijebiti za ublažavanje nepoželjnog djelovanja antinutritivnih polifenola što se nalaze u lišću drveća za bršćenje