Design and implementation of a data-driven intelligent water heating system for an island community: A case study

Abstract

Water heating accounts for approximately 25% of household energy use in developed countries. Therefore, the optimal control of water heating through the deployment of intelligent residential Electric Water Heaters (EWH) brings significant benefits. This paper presents an innovative design and implementation of an easy-to-use device for intelligent residential water heating. The device relied upon machine learning techniques to forecast a consumer’s hot water demand and optimize the operation of an EWH using a novel data collection process that relied on non-intrusive vibration data alone. The device was deployed in a six-month pilot project on the island of São Miguel, Portugal. The major difficulties were the novel use of vibration data to forecast the volume of hot water used and the uncertain behavior of the consumers. The challenges of only using vibration data were solved by careful data collection and artificial intelligence methods. To tackle the issue of uncertain consumer behavior, an innovative ‘heat now’ function was incorporated into the device to override the novel control framework. Results show that the device could accurately forecast hot water demand and optimally operate the EWH to meet this demand. The results showed an average reduction of 1.33 kWh/day per consumer, which equates to an average decrease of 35.5% in water heating costs. Calculations show that these devices can reduce the total energy used by 2832 kWh daily or 0.21% of total electricity generated. Furthermore, a fleet of these devices could reduce thermal generation by 0.37%, reducing emissions by 693.31 tons of CO2 per year. The results from the consumer survey show that the device did not affect the consumer’s comfort, validating the benefits and efficacy of the proposed device. Hence, the paper shows that a simple-to-use, novel device using an innovative forecasting algorithm based on non-intrusive vibration data brings numerous quantifiable benefits to actual consumers and electrical utilities.