The purpose of this paper is to provide a source of information on thermal energy use in buildings, its drivers, and their past, present and future trends on a global and regional basis. Energy use in buildings forms a large part of global and regional energy demand. The importance of heating and cooling in total building energy use is very diverse with this share varying between 18% and 73%. Biomass is still far the dominant fuel when a global picture is considered; the role of electricity is substantially growing, and the direct use of coal is disappearing from this sector, largely replaced by electricity and natural gas in the most developed regions. This paper identifies the different drivers of heating and cooling energy demand, and decomposes this energy demand into key drivers based on a Kaya identity approach: number of households, persons per household, floor space per capita and specific energy consumption for residential heating and cooling; and GDP, floor space per GDP, and specific energy consumption for commercial buildings. This paper also reviews the trends in the development of these drivers for the present, future – and for which data were available, for the past – in 11 world regions as well as globally. Results show that in a business-as-usual scenario, total residential heating and cooling energy use is expected to more or less stagnate, or slightly decrease, in the developed parts of the world. In contrast, commercial heating and cooling energy use will grow in each world region. Finally, the results show that per capita total final residential building energy use has been stagnating in the vast majority of world regions for the past three decades, despite the very significant increases in energy service levels in each of these regions.

Heating and cooling energy trends and drivers in buildings

In this paper, we propose a deep learning framework based on randomized neural network. In particular, inspired by the principles of Random Vector Functional Link (RVFL) network, we present a deep RVFL network (dRVFL) with stacked layers. The parameters of the hidden layers of the dRVFL are randomly generated within a suitable range and kept fixed while the output weights are computed using the closed form solution as in a standard RVFL network. We also propose an ensemble deep network (edRVFL) that can be regarded as a marriage of ensemble learning with deep learning. Unlike traditional ensembling approaches that require training several models independently from scratch, edRVFL is obtained by training a single dRVFL network once. Both dRVFL and edRVFL frameworks are generic and can be used with any RVFL variant. To illustrate this, we integrate the deep learning networks with a recently proposed sparse-pretrained RVFL (SP-RVFL). Extensive experiments on benchmark datasets from diverse domains show the superior performance of our proposed deep RVFL networks.

Random Vector Functional Link Neural Network based Ensemble Deep Learning

Effective energy consumption forecasting using empirical wavelet transform and long short-term memory

Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems

Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks

This paper describes the release of the detailed building operation data, including electricity consumption and indoor environmental measurements, of the seven-story 11,700-m2 office building located in Bangkok, Thailand. The electricity consumption data (kW) are that of individual air conditioning units, lighting, and plug loads in each of the 33 zones of the building. The indoor environmental sensor data comprise temperature (°C), relative humidity (%), and ambient light (lux) measurements of the same zones. The entire datasets are available at one-minute intervals for the period of 18 months from July 1, 2018, to December 31, 2019. Such datasets can be used to support a wide range of applications, such as zone-level, floor-level, and building-level load forecasting, indoor thermal model development, validation of building simulation models, development of demand response algorithms by load type, anomaly detection methods, and reinforcement learning algorithms for control of multiple AC units. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12527219

/pdf/cu-bems-smart-building-electricity-consumption-and-indoor-2oshj4p7gt.pdf

CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets

Music, an integral part of our lives, which is not only a source of entertainment but plays an important role in mental well-being by impacting moods, emotions and other affective states. In this age of Big Data, online music streaming services allow us to capture ecologically valid music listening behavior and provide a rich source of information to identify several user-specific aspects. Music preferences and listening strategies have been shown to be associated with the psychological well-being of listeners including internalized symptomatology and depression. However, till date no studies exist that examine music-related social tags, static & time-varying audio-derived features, in terms of acoustic and emotional content, and its association with users well-being. The current work aims at unearthing static and dynamic patterns and trends in the individuals at risk for depression as it manifests in naturally occurring online music consumption. Mental well-being scores and listening histories of 541 Last.fm users were examined. Social tags associated with each listener’s most popular tracks were analyzed to unearth the mood/emotions and genres associated with the users. Results revealed that social tags prevalent in the users at risk for depression were predominantly related to emotions depicting Sadness associated with genre tags representing neo-psychedelic-, avant garde-, dream-pop. Static and dynamic acoustic and emotion-related features were extracted from each user’s listening history and correlated with their mental well-being scores. Results revealed that individuals with greater depression risk resort to higher dependency on music with greater repetitiveness in their listening activity. Furthermore, the affinity of depressed individuals towards music that can be perceived as sad was found to be resistant to change over time. This study has large implications for future work in the area of assessing mental illness risk by exploiting digital footprints of users via online music streaming platforms. This study will open up avenues for an MIR-based approach to characterizing and predicting risk for depression which can be helpful in early detection and additionally provide bases for designing music recommendations accordingly.

Gopal Chitalia

Papers

Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks

CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets

Online Music Consumption Characterizing Depression Risk