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Energy consumption optimisation in buildings

Introduction

Given the current trends in energy prices and conservation of the environment, the management of energy use in buildings is a critical concern. This paper focuses on how buildings are responsible for about 40% of the energy consumption globally and how there is still room for improvement in terms of efficiency. ML and AI have been gradually applied to the energy field to solve such a problem. Through the analysis of big data, such technologies are capable of forecasting and managing energy consumption habits thus reducing the costs and emissions of CO2. This article aims to discuss the various issues, artificial intelligence answers, gains and practical uses of ML and AI in the management of energy consumption in buildings.

Challenges

The major barriers to effective energy consumption management in buildings can be categorized into three main groups: the problems in data, the problems in systems, and the problems in predictions. Some of the challenges are the absence of real-time energy consumption information, integrations of various building systems and the challenge in determining energy consumption trends. Other challenges include; the expenses that come with the implementation of energy saving measures, the absence of standard protocols for data collection and analysis and the need for a strong legal requirement for energy efficiency. Also, there is a low level of knowledge and perception of the advantages of energy conservation with regard to buildings.

AI Solutions

There are different ways in which AI and ML can address these problems. Real time energy consumption data can be processed by these technologies to detect flaws and provide possible remedies. It can also learn the consumption trends of energy and make recommendations depending on the past data and other conditions like weather and occupancy. Building systems can also be automated by AI to manage energy consumption effectively. For instance, DeepMind AI, an AI powered by Google has been applied in Google data centers and has cut down the energy used for cooling by as much as 40%. For instance, the startup Verdigris Technologies has created an AI-based system that employs machine learning to monitor and manage energy consumption in buildings.

Benefits

There are many advantages of incorporating AI and ML in the management of energy consumption in buildings such as lower costs, less carbon footprint, and better performance. The optimization of energy use can therefore enable buildings to cut their energy consumption, and thereby costs by as much as 30%. Also, AI and ML can enhance the reliability and the lifespan of building systems through identifying and solving problems. They also contribute to enhanced comfort and increased productivity through regulating proper indoor temperature and lighting.

Return on Investment

The ROI of applying AI and ML in the management of energy consumption in buildings can be quite impressive. A study by the American Council for an Energy-Efficient Economy revealed that energy efficiency measures provide an average return on investment of 13%. Moreover, the cost of energy can be recovered through reduced energy consumption and the investment made in AI and ML within a few years. For instance, Google’s DeepMind AI became profitable within a year of implementation due to energy conservation.

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