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Thermal comfort prediction is vital in achieving a good indoor environment and efficient energy management. However, thermal comfort is strongly nonlinear and dynamically changing over time, making it difficult to predict thermal comfort accurately. Two real-time thermal comfort prediction models on multi-time scales are proposed based on deep learning algorithms. The indirect prediction model for thermal comfort integrates the bidirectional long and short-term memory network to predict...

The aim of this study is to present a novel data-driven approach developed for space heating energy demand calculation of the whole EU building stock. To develop a computationally efficient bottom-up model that takes into account building physics parameters and details of the building stock make-up, an artificial neural network (ANN) is trained on a dataset of precise building-physics models. For this purpose, 2025 building energy simulations were performed in this study, ensuring...

With increasing energy consumption, how to achieve the energy-saving operation of air-conditioning systems is crucial for improving the energy efficiency of buildings. The accurate and reliable energy consumption prediction of air-conditioning systems can be useful for optimizing the energy supply and equipment operation strategies. However, most existing studies focus on the prediction of the long-term energy consumption of air-conditioning systems, which usually exceeds the individual...

Measurement of PM2.5 is carried out simultaneously outdoors as well as indoors using a Compact and Useful PM2.5 Instrument (CUPI) from Nov 2018 to Oct 2019. A high-time resolution data (~ 2 min interval) was recorded at Dwarka (West Delhi), New Delhi, to investigate the difference between outdoor and indoor ambient particulate matter. Year-long observations provide an opportunity to decipher that indoor PM2.5 concentration increases significantly from mid-November 2018 to mid-December 2018....

Climate change is the greatest challenge of the 21st century. It is threatening all aspects of societal life. The right to education of children is negatively impacted by these climate-induced challenges. This chapter presents findings of a study on the effects of climate change on the education of the younger generation. The study used a mixed research design method and was done within Kisii Municipality, Kenya. The findings were that climate change and its effects have brought about...

The effects of climate change are a major global concern. In Kenya, the impact of climate change has been felt in perennial droughts which sometimes result into conflict as communities fight for pasture and water. In a bid to address climate change issues, Kenya has developed policies that focus on mitigation and adaptation measures borrowed from ideas outlined in global climate change strategies. One such strategy is the employment of education as a tool to create awareness on climate...

With the increasing incidence of power blackouts attributed to climate change, climate-resilient load forecasting is increasingly necessary to enable timely network reconfiguration during extreme weather events. This paper proposes a generalised multi-factor Deep Learning (DL) model to forecast electricity load in Distribution Networks (DNs) during extreme climate periods. We optimise factors that affect forecast accuracy, including input matrix structures, calendar effects, and...

With the increasing incidence of power blackouts attributed to climate change, climate-resilient load forecasting is increasingly necessary to enable timely network reconfiguration during extreme weather events. This paper proposes a generalised multi-factor Deep Learning (DL) model to forecast electricity load in Distribution Networks (DNs) during extreme climate periods. We optimise factors that affect forecast accuracy, including input matrix structures, calendar effects, and...

Climate change is seen as a peril to the overall financial system, yet this revelation is in its infant stage. On that note, this study investigates the impact of climate change shocks on banking system resilience in selected Sub-Saharan economies. The study relies on a quantitative research method by first employing a Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) (1,1) model to forecast the volatility series of the climate change variables. Further, the study applies...

Deep learning models have been increasingly applied in the field of solar radiation prediction. However, the characteristics of a deep learning black box model restrict its application in practical scenarios such as model predictive control. Because energy system controllers may be unable to make final decisions based solely on the predictions of a black-box model. This study considers both the temporal and spatial dependencies of solar radiation predictions through unfolding sequences and...

There is an important gap in regional information on climate change and health, limiting the development of science-based climate policies in South American countries. This study aims to identify the main gaps in the existing scientific literature on the impacts, exposure, and vulnerabilities of climate change on population health. A scoping review was performed guided by four sub-questions focused on the impacts of climate change on physical and mental health, exposure and vulnerability...

Individuals globally spend about 88–92% of their entire time in the indoor environment. The implementation of regularly scheduled systems operation is common in many commercial and residential building types. Occupant Behavior (OB) is highly stochastic, making it difficult to depict the human factor using simple schedules. In the present work, window-opening tendencies were found to be highest in summer and transition seasons and lowest during winter. The Air Changes per Hour (ACH) value...

The climate system is an excellent example of a “complex system” since there is an interplay and inter-relation of several climate variables. Variables such as the Standardized Precipitation Index (SPI) are used to indicate the drought climate condition – a negative (or positive) value of SPI would imply a dry (or wet) state in a region. It is difficult to identify the factors influencing the SPI or their inter-relations (including feedback loops). Here, we aim to study the complex dynamics...

The climate system is an excellent example of a “complex system” since there is an interplay and inter-relation of several climate variables. Variables such as the Standardized Precipitation Index (SPI) are used to indicate the drought climate condition – a negative (or positive) value of SPI would imply a dry (or wet) state in a region. It is difficult to identify the factors influencing the SPI or their inter-relations (including feedback loops). Here, we aim to study the complex dynamics...