Razak Olu-Ajayi

• Doctor of Philosophy (in view)
• Machine Learning Engineer at University of Hertfordshire

Artificial intelligence and its subfields, such as machine learning, robotics, optimisation, knowledge-based systems , reality capture and extended reality, have brought remarkable advancements and transformative changes to various industries, including the building deconstruction industry. Acknowledging AI's benefits for decon-struction, this pape...

A prerequisite for decreasing the intensification of energy in buildings is to evaluate and understand the influencing factors of building energy performance (BEP). These factors include building envelope features and outdoor climactic conditions, among others. Based on the importance of the influencing factors in the development of the building en...

The application of statistical and artificial intelligence (AI) tools in building energy prediction (BEP) is considered one of the most effective advances towards improving energy efficiency. Thus, researchers are constantly propagating the energy prediction field with many prediction models using diverse statistical and AI tools. However, many of...

In recent years, there has been a surge in the global digitization of corporate processes and concepts such as digital technology development which is growing at such a quick pace that the construction industry is struggling to catch up with latest developments. A formidable digital technology, artificial intelligence (AI), is recognized as an esse...

The advancement of smart meters using evolving technologies such as the Internet of Things (IoT) is producing more data for the training of energy prediction models. Since many machine learning techniques were not premeditated to handle a large number of irrelevant features, it has engendered the search for optimal techniques to decrease the genera...

The development of data-driven building energy consumption prediction models has gained more attention in research due to its relevance for energy planning and conservation. However, many studies have conducted the inappropriate application of data-driven tools for energy consumption prediction in the wrong conditions. For example, employing a data...

Building energy efficiency is vital, due to the substantial amount of energy consumed in buildings and the associated adverse effects. A high-accuracy energy prediction model is considered as one of the most effective ways to understand building energy efficiency. In several studies, various machine learning models have been proposed for the predic...

Purpose Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction. De...

The substantial amount of energy consumption in buildings and the associated adverse effects prompts the importance of understanding building energy efficiency. Developing an energy prediction model with high accuracy is considered one of the most effective approach to understanding building energy efficiency. Therefore, various studies have develo...

The high proportion of energy consumed in buildings has engendered the manifestation of many environmental problems which deploy adverse impacts on the existence of mankind. The prediction of building energy use is essentially proclaimed to be a method for energy conservation and improved decision-making towards decreasing energy usage. Also, the c...

The consumption of energy in buildings has elicited the occurrence of many environmental problems such as air pollution. Building energy consumption prediction is fundamental for improved decision-making towards regulating or decreasing energy usage. There have been several applications of Machine Learning (ML) algorithms for predicting the energy...

In the effort to achieve accurate air pollution predictions, researchers have contributed various methodologies with varying data and different approaches that can be judged accurate in their respective contexts. Diverse approaches have been used so far in the literature to achieve optimal accuracy in the prediction of air pollution. Researchers ha...

A presentation on the paper titled "Air Pollution Prediction using Machine Learning – A Review"

In the past decades, the demand for energy in buildings has considerably amplified due to the increase in population and prompt urbanization. The high proportion of energy consumed by buildings engender major environmental problems causing climate change, air pollution and thermal pollution, which is detrimental to human existence. Therefore, the d...