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

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

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

CALISTO: A Cryogenic Far-Infrared/Submillimeter Observatory

We present a design for a cryogenically cooled large aperture telescope for far-infrared astronomy in the wavelength range 30 micrometers to 300 micrometers. The Cryogenic Aperture Large Infrared Space Telescope Observatory, or CALISTO, is based on an off-axis Gregorian telescope having a 4 m by 6 m primary reflector. This can be launched using an Atlas V 511, with the only optical deployment required being a simple hinged rotation of the secondary reflector. The off-axis design, which includes a cold stop, offers exceptionally good performance in terms of high efficiency and minimum coupling of radiation incident from angles far off the direction of maximum response. This means that strong astronomical sources, such as the Milky Way and zodiacal dust in the plane of the solar system, add very little to the background. The entire optical system is cooled to 4 K to make its emission less than even this low level of astronomical emission. Assuming that detector technology can be improved to the point where detector noise is less than that of the astronomical background, we anticipate unprecedented low values of system noise equivalent power, in the vicinity of 10(exp -19) WHz(exp -0.5), through CALISTO's operating range. This will enable a variety of new astronomical investigations ranging from studies of objects in the outer solar system to tracing the evolution of galaxies in the universe throughout cosmic time