Predictive control based on occupant behavior prediction for domestic hot water system using data mining algorithm

Abstract

Domestic hot water systems are a primary source of energy consumption in buildings, and present a promising future in terms of energy saving. Predictive control methods have been used to reduce energy consumption during an operation period. However, current methods lack consideration of occupant behavior, which significantly influences the prediction results. In this study, a data‐based predictive method is proposed to predict the shower behavior of occupants. A dataset was collected from seven occupants and was trained using support vector machine (SVM) to learn their showering habits. These results were used to predict the hot water demand. A comparative analysis shows that, in this way, the prediction results are more reliable under predictive control than predicting the hot water demand directly using SVM. A simulation of a domestic hot water system was then conducted. The results indicate that the proposed approach can reduce the heat loss from a hot water storage tank (ST) by up to 33% compared to a traditional control method, while maintaining a high assurance of the hot water supply (an insufficient hot water supply of less than 1%) with little change to the original system.

Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions

“…Support Vector Machines (SVM) are useful for prediction problems because they can handle both linear and non-linear data [160]. SVMs are also less prone to overfitting and produce solid results even with small sample sizes [24,26,139,161,164]. Notwithstanding, the performance is largely dependent on a suitable kernel and parameter adjustment, which can be a difficult undertaking.…”

“…✓ Shan et al [42] ✓ ✓ K. Ahmed et al [1] ✓ ✓ George et al [36] ✓ ✓ ✓ Edwards et al [52] ✓ ✓ ✓ ✓ ✓ K. Ahmed et al [4] ✓ ✓ Chmielewska et al [53] ✓ ✓ de Santiago et al [26] ✓ Ferrantelli et al [43] ✓ ✓ ✓ Fuentes et al [2] ✓ ✓ ✓ ✓ Marszal et al [6] ✓ ✓ ✓ Rouleau et al [37] ✓ ✓ ✓ Ivanko et al [5] ✓ ✓ ✓ De Simone et al [29] ✓ ✓ ✓ ✓ ✓ ✓ Xie and Noor [54] ✓ ✓ ✓ ✓ Tolofari et al [55] ✓ ✓ ✓ Mostafaeipour et al [56] ✓ ✓ ✓ ✓ Meireles et al [57] ✓ C. Chen et al [58] ✓ ✓ ✓ ✓ Alipour et al [59] ✓ ✓ ✓ Sarabia-Escriva et al [28] ✓ ✓ ✓ a Influencing factor considered by authors.…”

“…Research has shown that the most accurate predictions are made using real-time data collected over time on hourly or daily consumption rather than monthly and annually because user behaviour in relation to energy (kWh) consumption changes as a result 2 of 32 of influencing factors [1,4,[24][25][26][27][28]. Kadir et al review reveals that 84% of the studies focus on short-term energy consumption prediction due to its direct relevance to daily building operations.…”

Sustainability matchmaking: Linking renewable sources to electric water heating through machine learning

“…The proposed model predicts the cumulative hot water usage for the next day by taking into account of recent trends of historical data. Cao et al [101] used SVM to predict the shower behavior of occupants to forecast the hot water usage. The authors collected data from seven occupants and the results showed that, compared to a traditional control method, the proposal can reduce heat loss by up to 33%.…”

Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea

“…Data mining has emerged as an alternative tool for modeling and forecasting due to its ability to capture the non-linearity in the data. While the shortcoming of data mining is a large amount of training data [14], with the advent of big data era, data mining has recently been widely used for demand forecasting in the various fields, where data can be collected easily, such as energy [10,15], tourism [16,17], transportation [18][19][20], water management [21,22], remanufacturing [23], bike sharing [24,25], retail pharmacies [26], hospitals [27,28], logistics [14], and spare parts management [14,[29][30][31], showing its usefulness.…”