Addressing the thermal performance gap: Possible performance control tools for the construction manager

Construction practice has failed to deliver buildings that consistently meet their expected thermal performance; however, examples of good practice do exist. Buildings can be designed and built within acceptable tolerances and meet nearly zero carbon standards. Unfortunately, due to the negative implications associated with the performance gap there have been attempts to divert attention from measurement, with some being critical of methods that were used to identify the variance in building performance. However, the tools have proven reliable and the practice of thermal measurement which was once limited to scientists is finding its place in industry. Measurement is becoming more accepted and different tools are being used to assess thermal performance. The tools can add value to inspections, building surveys and assist with quality control. Construction professionals, not least construction managers, are gaining valuable insights through research undertaken and observations gained. The tests reviewed provide new methods of capturing evidence on building performance, thus allowing valuable information on the quality of design, workmanship and process to be gained. Use of thermal measurement and analysis tools should result in further improvements to building performance. The data from major performance evaluation projects are reviewed and presente

A neural network cost function for highly class-imbalanced data sets

We introduce a new cost function for the training of a neural network classifier in conditions of high class imbalance. This function, based on an approximate confusion matrix, represents a balance of sensitivity and specificity and is thus well suited to problems where cost functions such as the mean squared error and cross entropy are prone to overpredicting the majority class. The benefit of the new measure is shown on a set of common class-imbalanced datasets using the Matthews Correlation Coefficient as an independent scoring measure

Pseudo-analytical solutions for stochastic options pricing using monte carlo simulation and breeding PSO-trained neural networks

We introduce a novel methodology for pricing options which uses a particle swarm trained neural network to approximate the solution of a stochastic pricing model. The performance of the network is compared to the analytical solution for European call options and the errors shown statistically comparable to Monte Carlo pricing. The work provides a proof of concept that can be extended to more complex options for which no analytical solutions exist, the pricing method presented here delivering results several orders of magnitude faster than the Monte Carlo pricing method used by default in the financial industry

Adding value and meaning to coheating tests

Purpose: The coheating test is the standard method of measuring the heat loss coefficient of a building, but to be useful the test requires careful and thoughtful execution. Testing should take place in the context of additional investigations in order to achieve a good understanding of the building and a qualitative and (if possible) quantitative understanding of the reasons for any performance shortfall. The paper aims to discuss these issues. Design/methodology/approach: Leeds Metropolitan University has more than 20 years of experience in coheating testing. This experience is drawn upon to discuss practical factors which can affect the outcome, together with supporting tests and investigations which are often necessary in order to fully understand the results. Findings: If testing is approached using coheating as part of a suite of investigations, a much deeper understanding of the test building results. In some cases it is possible to identify and quantify the contributions of different factors which result in an overall performance shortfall. Practical implications: Although it is not practicable to use a fully investigative approach for large scale routine quality assurance, it is extremely useful for purposes such as validating other testing procedures, in-depth study of prototypes or detailed investigations where problems are known to exist. Social implications: Successful building performance testing is a vital tool to achieve energy saving targets. Originality/value: The approach discussed clarifies some of the technical pitfalls which may be encountered in the execution of coheating tests and points to ways in which the maximum value can be extracted from the test period, leading to a meaningful analysis of the building's overall thermal performance

Deep Probabilistic Modelling of Price Movements for High-Frequency Trading

In this paper we propose a deep recurrent architecture for the probabilistic modelling of high-frequency market prices, important for the risk management of automated trading systems. Our proposed architecture incorporates probabilistic mixture models into deep recurrent neural networks. The resulting deep mixture models simultaneously address several practical challenges important in the development of automated high-frequency trading strategies that were previously neglected in the literature: 1) probabilistic forecasting of the price movements; 2) single objective prediction of both the direction and size of the price movements. We train our models on high-frequency Bitcoin market data and evaluate them against benchmark models obtained from the literature. We show that our model outperforms the benchmark models in both a metric-based test and in a simulated trading scenario

External walls partially filled with insulation, and the potential to "top-up" the residual cavity.

This review found that the connecting voids in partially filled cavity walls leads to considerable variation in thermal performance. Whilst photographic records found considerable evidence of gaps in the insulation resulting from poor site practice and installation, research also shows that relatively small breaks between insulation sheets or gaps between the wall and insulation result in a thermal bypass. As the gaps and connecting voids increase air circulation, convection currents and pressure induced exchanges reduce the effectiveness of the thermal barrier. Where effective installation is possible, the topping up of partially filled cavity walls with insulation shows potential to improve the thermal performance of the wall. In the cases reviewed, the installation of blown mineral wool fill reduced variation in heat flow and increased thermal performance. By filling the voids with insulation the passage of air and thermal bypasses were restricted

Application of stochastic recurrent reinforcement learning to index trading

A novel stochastic adaptation of the recurrent reinforcement learning (RRL) methodology is applied to daily, weekly, and monthly stock index data, and compared to results obtained elsewhere using genetic programming (GP). The data sets used have been a considered a challenging test for algorithmic trading. It is demonstrated that RRL can reliably outperform buy-and-hold for the higher frequency data, in contrast to GP which performed best for monthly data

Party Wall Cavity Barrier Effective Edge Seal Testing for ARC Building Solutions Ltd

ARC Building Solutions Ltd manufacture, market and distribute a range of party wall cavity barriers. Part L of the Building Regulations (HM Government, 2013) stipulates that when cavity barriers are used for edge sealing purposes, then the seal must be effective at restricting air flow between the party wall cavity and the external wall cavity or external environment (Figure 1). The Building Control Alliance (2011) describes how an edge seal is to be judged as being effective in a qualitative manner. However, there is currently no standard test for quantitatively demonstrating the effectiveness of edge sealing using a cavity barrier product. ARC Building Solutions Ltd wished to quantify the effectiveness of the edge seal that could be achieved using the Company’s products under test conditions. This information could prove useful when engaging designers, building control bodies and warranty providers. As there is currently no quantitative benchmark for what is deemed to be an effective edge seal this project aimed to compare the performance of a recognised ‘current practice’ solution against ARC Building Solutions Ltd.’s T-Barrier, and as far as possible compare these to an accepted effective edge seal for a number of different party wall and external wall cavity widths. In addition to this comparative testing, this project may also assist in the development and application of a standardised ‘Edge Seal Test’ for which there is understood to be no current standard or specific precedent. Whilst the test rig may not be fully representative of the actual construction of a party wall/external wall junction in situ, it is hoped that the results may provide insight as to how the performance of these products may compare in real building situations

Identification of diverse database subsets using property-based and fragment-based molecular descriptions

This paper reports a comparison of calculated molecular properties and of 2D fragment bit-strings when used for the selection of structurally diverse subsets of a file of 44295 compounds. MaxMin dissimilarity-based selection and k-means cluster-based selection are used to select subsets containing between 1% and 20% of the file. Investigation of the numbers of bioactive molecules in the selected subsets suggest: that the MaxMin subsets are noticeably superior to the k-means subsets; that the property-based descriptors are marginally superior to the fragment-based descriptors; and that both approaches are noticeably superior to random selection

A New Methodology to Exploit Predictive Power in (Open, High, Low, Close) Data

Prediction of financial markets using neural networks and other techniques has predominately focused on the close price. Here, in contrast, the concept of a mid-price based on an Open, High, Low, Close (OHLC) data structure is proposed as a prediction target and shown to be a significantly easier target to forecast, suggesting previous works have attempted to extract predictive power from OHLC data in the wrong context. A prediction framework incorporating a factor discovery and mining process is developed using Randomised Decision Trees, with Long Short Term Memory Recurrent Neural Networks subsequently demonstrating remarkable predictive capabilities of up to 50.73% better than random (75.42% accuracy) on hourly data based on the FGBL German Bund futures contract, and 42.5% better than random (72.04% accuracy) on a comparison Bitcoin dataset