Improving Energy Consumption Monitoring in Food and Drinks Manufacturing and Storage Systems with Predictive Machine Learning

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The projected surge in the global population and the increasing demand for food present a significant sustainability challenge in the face of relatively slower growth in energy production. The food and drink industry, being the largest manufacturing sector in the United Kingdom, faces substantial sustainability strains due to its significant energy consumption and greenhouse gas emissions. This PhD aims to address these challenges by leveraging predictive machine learning techniques to improve energy consumption monitoring in food and drinks manufacturing and storage systems. This PhD comprises three interconnected studies, each focusing on aspects of energy consumption and prediction within the food and drinks manufacturing and cold storage environments. Based on weather data, the first study aims to develop explainable machine learning techniques for predicting electricity consumption, indoor temperature, and indoor humidity. This research investigates the challenges presented by current models in this specialised domain and explores the integration of seasonal elements and human behaviour into these predictive models. Accurate prediction of electricity consumption can greatly contribute to reducing energy usage through improved monitoring and planning. This optimisation can help streamline operational scheduling in such environments, facilitating more sustainable energy management practices and ultimately reducing CO2 emissions. Additionally, monitoring temperature and humidity in such settings is crucial to ensure food safety and preserve the quality of perishable items. Building upon the first study, the second study aims to develop predictive models for not only electricity but also gas consumption in a food manufacturing environment based on production demand and weather conditions. By integrating production schedules with weather conditions, these models have the potential to optimise resource utilisation and reduce costs. The third study focuses on developing a real-time anomaly detection system for gas and electricity consumption in food manufacturing environments, aiming to minimise energy waste and improve operational efficiency. Through an applied science approach, this research aims to provide valuable insights for the food and drinks industry, ultimately improving operational efficiency. This will not only result in cost reduction but also contribute to global climate change mitigation efforts. The anticipated outcomes of this study hold good potential for tangible benefits to food and drink businesses, empowering them to make informed decisions and effectively minimise their energy consumption.