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One of the most important concerns affecting humanity today is climate change that has led to increased frequency of natural disasters that threaten social and economic stability to populations. Zambia’s vulnerability to the threat of disasters remains high because the country still lacks an effective Early Warning System (EWS). This study recognises the need to evaluate various Machine Learning (ML) algorithms, that have been successfully implemented in disaster prediction, in order to...

Recently, Occupant-Centric Control (OCC) strategies have gained mounting interest. Previous studies made use of OCC strategies for adjusting the operation of heating/cooling systems, improving indoor thermal comfort and governing mechanical ventilation systems. However, a very limited number of studies have applied OCC strategies to natural ventilation systems. Further, the feasibility of establishing OCC strategies for controlling indoor thermal comfort, energy use and specifically air...

Recently, Occupant-Centric Control (OCC) strategies have gained mounting interest. Previous studies made use of OCC strategies for adjusting the operation of heating/cooling systems, improving indoor thermal comfort and governing mechanical ventilation systems. However, a very limited number of studies have applied OCC strategies to natural ventilation systems. Further, the feasibility of establishing OCC strategies for controlling indoor thermal comfort, energy use and specifically air...

Simplification of input variables can increase the applicability of Artificial Intelligence (AI) in building load prediction. The most essential inputs for AI therefore need to be identified via a significance level test. In this study, the significance of the input parameters was evaluated using the standardized regression coefficient (SRC) and Explainable AI methods, i.e., Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). To consider various...

Children differ from adults in their physiology and cognitive ability. Thus, they are extremely vulnerable to classroom thermal comfort. However, very few reviews on the thermal comfort of primary school students are available. Further, children-focused surveys have not reviewed the state-of-the-art in thermal comfort prediction using machine learning (AI/ML). Consequently, there is a need for discussion on children-specific challenges in AI/ML-based prediction. This article bridges these...

Chapter 5 makes an attempt to predict trends in average land temperature due to CO2 emissions from all the non-renewable energy resources like “coal”, “oil”, “natural gas”, and “flaring”. The chapter illustrates the step-by-step process of time series analysis that is used to forecast temperature values. Predictions with the help of a machine learning model reveal that if no significant and immediate steps are implemented, emissions from these sources of energy will continue to rise in the...

To ensure continued food security and economic development in Africa, it is very important to address and adapt to climate change. Excessive dependence on rainfed agricultural production makes Africa more vulnerable to climate change effects. Weather information and services are essential for farmers to more effectively survive the increasing occurrence of extreme weather events due to climate change. Weather information is important for resource management in agricultural production and...

Climate change and other environmental disturbances are causing sea level rise all over the world. Due to sea-level rise and other unprecedented atmospheric phenomena caused by climate change, Indian coasts are vulnerable to coastal erosion. The Kerala coast, at the southern tip of India's west coast, has experienced a sea change in the last decade. To address coastal variations, we investigate coastal erosion, coastal accretion, and shoreline changes (from 2006 to 2020) along this coast...

Energy Poverty (EP) is a widespread problem in Europe. EP detection is hampered by a lack of data and global metrics. Recently, innovative approaches using Artificial Intelligent (AI) techniques have been increasingly applied for the EP alleviation. In this work, studies focused on the application of AI on EP were studied. It was identified that there is not a high number of works that apply AI to fight against EP (considering this problem as a multidimensional phenomenon). Artificial Neural...

Evolutionary algorithms and allied fields are getting more visibility as well as familiarity due to their numerous flexibilities such as handling high-dimensional non-linear problems and more. This book will help budding researchers to formulate their research problems, and comprises 10 chapters: three on optimization, five on machine learning algorithms, one on Internet of Things, and one on remote sensing.In Focus–a book series that showcases the latest accomplishments in water research....

The occupants' presence, activities, and behaviour can significantly impact the building's performance and energy efficiency. Currently, heating, ventilation, and airconditioning (HVAC) systems are often run based on assumed occupancy levels and fixed schedules, or manually set by occupants based on their comfort needs. However, the unpredictability and variability of occupancy patterns can lead to over/under the conditioning of space when using such approaches, affecting indoor air quality...

The occupants' presence, activities, and behaviour can significantly impact the building's performance and energy efficiency. Currently, heating, ventilation, and airconditioning (HVAC) systems are often run based on assumed occupancy levels and fixed schedules, or manually set by occupants based on their comfort needs. However, the unpredictability and variability of occupancy patterns can lead to over/under the conditioning of space when using such approaches, affecting indoor air quality...

Personal thermal comfort models are crucial for the future of human-in-the-loop HVAC control in energy-efficient buildings. Individual comfort models, compared to average population responses, can provide the personalization required for successful control. In this work, we frame the thermal preference prediction task as a multivariate, multi-class classification problem and use deep learning and time-series-based approach for thermal preference prediction. We combine l1 regularization with...

Personal thermal comfort models are crucial for the future of human-in-the-loop HVAC control in energy-efficient buildings. Individual comfort models, compared to average population responses, can provide the personalization required for successful control. In this work, we frame the thermal preference prediction task as a multivariate, multi-class classification problem and use deep learning and time-series-based approach for thermal preference prediction. We combine l1 regularization with...

Building Energy prediction has emerged as an active research area due to its potential in improving energy efficiency in building energy management systems. Essentially, building energy prediction belongs to the time series forecasting or regression problem, and data-driven methods have drawn more attention recently due to their powerful ability to model complex relationships without expert knowledge. Among those methods, artificial neural networks (ANNs) have proven to be one of the most...

A large part of energy consumption in homes, offices and commercial spaces is related to Heating, Ventilation and Air-conditioning (HVAC) devices. The effective parameter on the consumption of HVAC systems is internal heat gains that arise from occupants, electric equipment and lighting. In order to reduce the energy consumption of these systems, internal heat gains should be predicted accurately. Since there are few investigations performed on the prediction of internal heat gains, in this...

Climate change in India is one of the most alarming problems faced by our community. Due to adverse and sudden changes in climate in past few years, mankind is at threat. Various impacts of climate change include extreme heat, changing rainfall patterns, droughts, groundwater, glacier melt, sea-level rise, and many more. Machine Learning can be used to analyze and predict the graph of change using previous data and thus design a model which in the future can furthermore be used to catalyze...