Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

Separation and purification of rebaudioside A from extract of Stevia Rebaudiana leaves by macroporous adsorption resins

The separation and purification of rebaudioside A from Stevia rebaudiana crude extracts (Steviosides) by macroporous resin were optimized by Taguchi orthogonal array (OA) experimental design methodology. This approach was applied to evaluate the influence of five factors (adsorption temperature, desorption time, elution solution ratio, adsorption volume and type of resin) on the rebaudioside A yield. The percentage contribution of each factor was also determined. The results showed that elution solution ratio and adsorption volume made the greatest (59.6%) and the lowest (1.3%) contribution, respectively. The results showed that the Taguchi method is able to model the purification of rebaudioside A process well (R2 > 0.998) and can therefore be applied in future studies conducted in various fields. Adsorption temperature 35°C, desorption time 60min, elution solution ratio 3, adsorption volume 200ml and HPD-400 as resin were the best conditions determined by the Taguchi method

Unsupervised learning for feature projection: Extracting patterns from multidimensional building measurements

Data visualization is an important resource for decision makers to obtain information from large datasets. Based on the data obtained from either predictions or measurements, different strategies are combined and tested to reduce the energy demand, whilst keeping the indoor comfort at suitable level. Although the information expressed from data representation can significantly influence the decisions, little research has focused on extracting features from building measurements. This paper provides an in-depth view into representation of building data, and applies three dimensionality reduction algorithms Principle Component Analysis (PCA), autoencoder and t-Distributed Stochastic Neighbour Embedding (t-SNE) on measurements from a teaching building. Results show that whilst PCA returns linear representations, it also has the least data compression, which can be useful for obtaining more general features. On the other hand, t-SNE returns the most compressed data, which is suitable for seeking large margins within a dataset. However, t-SNE may be unsuitable for datasets with recurring step-like temporal profiles. Autoencoder is the best overall option, as they capture the nonlinearities within a dataset whilst avoiding excessive data compression. Fine-tuning the hyperparameters of studied the algorithms, and the perils of relying on poorly tuned models is discussed at the end of the study

A Computational Method For Simulation Of Trunk Motion: Towards A Theoretical Based Quantitative Assessment Of Trunk Performance

Quantitative assessment of trunk muscle performance is important in documenting the extent of impairment and disability due to low back disorders (LBD). The statistical pattern recognition problem of classifying LBD patients and normal subjects based on dynamic trunk performance has been data driven. To provide clinical insight for interpretation of the distinctive features in the movement profiles, we have suggested an optimization-based approach for simulation of dynamic point-to-point sagittal trunk movement. The effect of strength impairment on movement patterns was simulated based on minimizing different physical cost functions: Energy, Jerk, Peak Torque, Impulse, and Work. During unconstrained simulations, uni-modal velocity patterns are predicted???, while time to peak velocity is distinct for each cost function. The significant differences between unimpaired optimal movement profiles were diminished by imposing an 80% reduction in extensor muscle strength. The results indicate ..

Methods and Tools for Urban Energy Planning

Cities are responsible for around 70% of global energy demand and are considered as having a crucial role in effective abatement of global energy consump-tions. The topic is largely discussed in available literature, which reveals the great diversity of applied approaches and the necessity to move towards the concept of smart energy systems, focussing on synergies among different energy sectors. How-ever, considering the large share of responsibility of the building sector, this chapter focussed on related energy demand assessment. After an initial introduction, which spotlights the most complex elements, the chapter presents some methods of analysis to evaluate the energy performance of the existing building stocks. Subsequently an overview is presented of the methods and tools for determining the energy demand of buildings within urban energy planning, paying particular attention to those that rely on hourly profiles. The assessment of hourly energy demand of the existing building stock, as well as the prediction of its variation due to energy efficiency measures, are fundamental activities for planning strategies of distributed generation, district heating and/or cooling networks, renewables integration, energy storages, etc., all necessary in moving towards smart energy districts