Random forests and artificial neural network for predicting daylight illuminance and energy consumption

Predicting energy consumption and daylight illuminance plays an important part in building lighting control strategies. The use of simplified or data-driven methods is often preferred where a fast response is needed e.g. as a performance evaluation engine for advanced real-time control and optimization applications. In this paper we developed and then compared the performance of the widely-used Artificial Neural Network (ANN) with Random Forest (RF), a recently developed ensemble-based algorithm. The target application was predicting the hourly energy consumption and daylight illuminance values of a classroom in Cardiff, UK. Overall, RF performed better than ANN for predicting daylight illuminance; with coefficients of determination (R^2) of 0.9881 and 0.9799 respectively. On the energy consumption testing dataset, ANN performed marginally better than RF with R^2 values of 0.9973 and 0.9966 respectively. RF performs internal cross-validation and is relatively easy to tune as it has few tuning parameters. The paper also highlighted possible future research directions

Solar energy and daylight on tilt planes under CIE standard skies

The CIE (International Commission on Illuminance) Standard Skies depicts the typical diffuse luminance and radiance distributions over the skydome. However, it can be challenging to interpret the luminance and radiance distribution into the irradiance and illuminance contributions on tilt planes by numerical integrations. This paper proposes a surrogate model that determines the ratio of the diffuse irradiance on an arbitrary tilt plane to that on the unobstructed horizontal plane without the complicated and time-consuming numerical integrations. The model is determined using solar altitude, sky conditions, and the angular distance of the plane (surface normal) and the sun. The proposed model is validated by measurement of the vertical illuminance and irradiance that are taken in 2004 and 2005, and the irradiance on planes of different tilt angles and azimuth directions from February to May in 2015. All measurements were in the 10-minute interval. For vertical planes, the proposed approach gives the ratio of the root mean square errors to the measurement average 1.38% to 2.04% lower than a classical model for irradiance and 3.6% to 4.6% for illuminance, when the Skies can be accurately identified. The model thus accurately interprets the luminance and radiance distributions of the CIE Standard Skies, which can be essential to a fast study for the solar energy potential as well as the thermal and daylight environments under different sky conditions

A Framework for Augmenting Building Performance Models Using Machine Learning and Immersive Virtual Environment

Building performance models (BPMs), such as building energy simulation models, have been widely used in building design. Existing BPMs are mainly derived using data from existing buildings. They may not be able to effectively address human-building interactions and lack the capability to address specific contextual factors in buildings under design. The lack of such capability often contributes to the existence of building performance discrepancies, i.e., differences between predicted performance during design and the actual performance. To improve the prediction accuracy of existing BPMs, a computational framework is developed in this dissertation. It combines an existing BPM with context-aware design-specific data involving human-building interactions in new designs by using a machine learning approach. Immersive virtual environments (IVEs) are used to acquire data describing design-specific human-building interactions, a machine learning technique is used to combine data obtained from an existing BPM, and IVEs are used to generate an augmented BPM. The potential of the framework is investigated and evaluated. An artificial neural network (ANN)-based greedy algorithm combines context-aware design-specific data obtained from IVEs with an existing BPM to enhance the simulations of human-building interactions in new designs. The results of the application show the potential of the framework to improve the prediction accuracy of an existing BPM evaluated against data obtained from the physical environment. However, it lacks the ability to determine the appropriate combination between context-aware design-specific data and data of the existing BPM. Consequently, the framework is improved to have ability to determine an appropriate combination based on a specified performance target. A generative adversarial network (GAN) is used to combine context-aware design-specific data and data of an existing BPM using the performance target as guide to generate an augmented BPM. The results confirm the effectiveness of this new framework. The performance of the augmented BPMs generated using the GAN-based framework is significantly better than the updated BPMs generated using the ANN-based greedy algorithm. The framework is completed by incorporating a robustness analysis to assist investigations of robustness of the GAN regarding the uncertainty involved in the input parameters (i.e., an existing BPM and context-aware design-specific data). Overall, this dissertation shows the promising potential of the framework in enhancing performance of BPMs and reducing performance discrepancies between estimations made during design and in performance in actual buildings

An experimental and analytical study of visual detection in a spacecraft environment, 1 July 1968 - 1 July 1969

Predicting star magnitude which can be seen with naked eye or sextant through spacecraft windo

A functional data analysis for assessing the impact of a retrofitting in the energy performance of a building

There is an increasing interest in reducing the energy consumption in buildings and in improving their energy efficiency. Building retrofitting is the employed solution for enhancing the energy efficiency in existing buildings. However, the actual performance after retrofitting should be analysed to check the effectiveness of the energy conservation measures. The aim of this work was to detect and to quantify the impact that a retrofitting had in the electrical consumption, heating demands, lighting and temperatures of a building located in the north of Spain. The methodology employed is the application of Functional Data Analyses (FDA) in comparison with classic mathematical techniques such as the Analysis of Variance (ANOVA). The methods that are commonly used for assessing building refurbishment are based on vectorial approaches. The novelty of this work is the application of FDA for assessing the energy performance of renovated buildings. The study proves that more accurate and realistic results are obtained working with correlated datasets than with independently distributed observations of classical methods. Moreover, the electrical savings reached values of more than 70% and the heating demands were reduced more than 15% for all floors in the building.Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C2

Adaptive Building Envelope: An Integral Approach to Indoor Environment Control in Buildings

The problem of energy consumption of buildings is complex and multidimensional, as it is a cross section of building envelope performance, indoor environmental conditions and user demands and preferences. In order to fulfil the EU goal stated in the 2020 climate and energy package and beyond, the implementation of high-performance buildings is crucial. Part of the solution is properly designed, flexible and adequately controlled building envelope that can contribute to reduced energy consumption and to increased occupancy comfort. In the presented chapter first, a structured treatment of the indoor environment formation is proposed that can be used in order to define appropriate fields of interventions when designing building automation systems. Furthermore, interaction between adaptive building envelope elements, indoor and exterior environment is discussed and elaborated. Second, the conventional and artificial intelligence control approaches used in building automation are discussed and commented, whereas advantages and disadvantages of each group are discussed. At the end, an example of building automation system designed on the principles of a holistic treatment of indoor environment in buildings is presented. The discussed system was designed at the Faculty of Civil and Geodetic Engineering using a combination of conventional and artificial intelligence control methods

A Functional Data Analysis for Assessing the Impact of a Retrofitting in the Energy Performance of a Building

There is an increasing interest in reducing the energy consumption in buildings and in improving their energy efficiency. Building retrofitting is the employed solution for enhancing the energy efficiency in existing buildings. However, the actual performance after retrofitting should be analysed to check the effectiveness of the energy conservation measures. The aim of this work was to detect and to quantify the impact that a retrofitting had in the electrical consumption, heating demands, lighting and temperatures of a building located in the north of Spain. The methodology employed is the application of Functional Data Analyses (FDA) in comparison with classic mathematical techniques such as the Analysis of Variance (ANOVA). The methods that are commonly used for assessing building refurbishment are based on vectorial approaches. The novelty of this work is the application of FDA for assessing the energy performance of renovated buildings. The study proves that more accurate and realistic results are obtained working with correlated datasets than with independently distributed observations of classical methods. Moreover, the electrical savings reached values of more than 70% and the heating demands were reduced more than 15% for all floors in the building.This paper was funded by the Spanish Government (Science, Innovation and Universities Ministry) under the project RTI2018-096296-B-C21