“…The first method’s ranking filters top principles, which the second method then refines with scores. This cascade approach, akin to dimensionality reduction simplifying the model’s complexity while maintaining robust predictive power [ 52 ], aligns with previous methodologies and is well-suited for handling categorical variables directly. It leverages each model’s strengths, sequentially refining the analysis for more precise, user-tailored recommendations.…”
Energy-related occupant behaviour in the built environment is considered crucial when aiming towards Energy Efficiency (EE), especially given the notion that people are most often unaware and disengaged regarding the impacts of energy-consuming habits. In order to affect such energy-related behaviour, various approaches have been employed, being the most common the provision of recommendations towards more energy-efficient actions. In this work, the authors extend prior research findings in an effort to automatically identify the optimal Persuasion Strategy (PS), out of ten pre-selected by experts, tailored to a user (i.e., the context to trigger a message, allocate a task or providing cues to enact an action). This process aims to successfully influence the employees’ decisions about EE in tertiary buildings. The framework presented in this study utilizes cultural traits and socio-economic information. It is based on one of the largest survey datasets on this subject, comprising responses from 743 users collected through an online survey in four countries across Europe (Spain, Greece, Austria and the UK). The resulting framework was designed as a cascade of sequential data-driven prediction models. The first step employs a particular case of matrix factorisation to rank the ten PP in terms of preference for each user, followed by a random forest regression model that uses these rankings as a filtering step to compute scores for each PP and conclude with the best selection for each user. An ex-post assessment of the individual steps and the combined ensemble revealed increased accuracy over baseline non-personalised methods. Furthermore, the analysis also sheds light on important user characteristics to take into account for future interventions related to EE and the most effective persuasion strategies to adopt based on user data. Discussion and implications of the reported results are provided in the text regarding the flourishing field of personalisation to motivate pro-environmental behaviour change in tertiary buildings.
“…The first method’s ranking filters top principles, which the second method then refines with scores. This cascade approach, akin to dimensionality reduction simplifying the model’s complexity while maintaining robust predictive power [ 52 ], aligns with previous methodologies and is well-suited for handling categorical variables directly. It leverages each model’s strengths, sequentially refining the analysis for more precise, user-tailored recommendations.…”
Energy-related occupant behaviour in the built environment is considered crucial when aiming towards Energy Efficiency (EE), especially given the notion that people are most often unaware and disengaged regarding the impacts of energy-consuming habits. In order to affect such energy-related behaviour, various approaches have been employed, being the most common the provision of recommendations towards more energy-efficient actions. In this work, the authors extend prior research findings in an effort to automatically identify the optimal Persuasion Strategy (PS), out of ten pre-selected by experts, tailored to a user (i.e., the context to trigger a message, allocate a task or providing cues to enact an action). This process aims to successfully influence the employees’ decisions about EE in tertiary buildings. The framework presented in this study utilizes cultural traits and socio-economic information. It is based on one of the largest survey datasets on this subject, comprising responses from 743 users collected through an online survey in four countries across Europe (Spain, Greece, Austria and the UK). The resulting framework was designed as a cascade of sequential data-driven prediction models. The first step employs a particular case of matrix factorisation to rank the ten PP in terms of preference for each user, followed by a random forest regression model that uses these rankings as a filtering step to compute scores for each PP and conclude with the best selection for each user. An ex-post assessment of the individual steps and the combined ensemble revealed increased accuracy over baseline non-personalised methods. Furthermore, the analysis also sheds light on important user characteristics to take into account for future interventions related to EE and the most effective persuasion strategies to adopt based on user data. Discussion and implications of the reported results are provided in the text regarding the flourishing field of personalisation to motivate pro-environmental behaviour change in tertiary buildings.
“… 24 It has been utilized to develop dynamic treatment regimens and provide a precise insulin dosage to react to the immediate needs of patients with diabetes. 23 Despite the rapid progress of ML methods, there are several potential flaws, including data bias, 27 overfitting, 28 resource-intensive training, 29 and limited transfer learning. 30 Figure 1 Current applications of machine intelligence in diabetes care and research Common algorithms used in supervised learning include 31 (1) artificial neural networks, such as Boltzmann machines, restricted Boltzmann machines, multi-layer perceptron, radial basis function networks, recurrent neural networks, Hopfield networks, convolutional neural networks, and spiking neural networks; (2) Bayesian learning, such as naive Bayes, Gaussian naive Bayes, multiple naive Bayes, average one-dependence estimators, Bayesian belief networks, and Bayesian networks; (3) decision trees, such as classification and regression tree, Iterative Dichotomiser 3, C4.5 algorithm, C5tree.0 algorithm, chi-squared automatic interaction detection, decision stump, and supervised learning in quest; (4) ensemble methods, such as random forest, bagging, boosting, AdaBoost, and XGBoost; and (5) linear models, such as linear regression, logistic regression, generalized linear models, Fisher linear discriminant analysis, quadratic discriminant analysis, least absolute shrinkage and selection operator regression, multi-modal logistic regression, naive Bayes classifier, and perceptron and linear support vector machine.…”
“…Thus, it is an example of the emerging paradigm of distributed intelligent systems and has become one of the most popular trends in smart industry, agriculture, healthcare, home, transportation and so forth 5 . Since EI‐enabled CPS provides novel distributed computing and processing ability and enables rapid machine‐to‐machine communication and machine‐to‐human interaction, EI assisted IoT takes localized processing farther away from the network right down to the sensor by pushing the computing processes even closer to the data sources, which also provides multidisciplinary novel solutions and interactions to improve QoS and QoE 6 …”
Section: Introductionmentioning
confidence: 99%
“…5 Since EI-enabled CPS provides novel distributed computing and processing ability and enables rapid machine-to-machine communication and machine-to-human interaction, EI assisted IoT takes localized processing farther away from the network right down to the sensor by pushing the computing processes even closer to the data sources, which also provides multidisciplinary novel solutions and interactions to improve QoS and QoE. 6 It is clear that EI-enabled CPS promotes a large class of applications and has emerged with a great potential to change our lives and improve user's QoE. However, EI also brings us new challenges, such as costs, communications, data moving and management, security and privacy issues.…”
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