Since heating-related energy consumption is one of the main sources of greenhouse gas emissions and accounts for a large part of domestic energy use in Europe, reducing heating-related energy consumption has great potential to reduce Europe’s greenhouse gas emissions. This study examines which factors determine people's intention to decrease heating-related energy usage, specifically lowering the temperature in winter. It was part of a larger European project focused on promoting energy reduction. This study presents a smaller-scale model tested among 363 individuals from five pilot countries, i.e., Belgium (n = 58), Croatia (n = 82), Germany (n = 105), Greece (n = 33), and Portugal (n = 85). We applied three robust theoretical frameworks: the Theory of Planned Behaviour, the Value Belief Norm Theory, and the Prototype Willingness Model. We conducted a confirmatory factor analysis to ensure construct validity, followed by a structural equation model. Our findings suggest that willingness (from the Prototype Willingness Model) is the most important predictor in explaining someone’s intention to reduce heating-related energy consumption. Additionally, perceived behavioural control, subjective norms, and attitudes (part of Theory of Planned Behaviour) also play an important role in predicting the intention to reduce energy consumption. Lastly, personal moral norms (from the Value Belief Norm Theory) have a significant impact, but their effect is moderate compared to the other predictors.

Reducing heating-related energy consumption is vital in Europe, where it accounts for a significant portion of domestic energy usage. We studied the factors that influence reduced heating-related consumption by using three theoretical frameworks: the Theory of Planned Behaviour, the Value Belief Norm theory, and the Prototype Willingness Model. Our sample consisted of 3098 people from 29 European countries. We conducted a confirmatory factor analysis to verify whether our observed variables measure our latent factors, followed by a structural equation model that incorporated these three behavioural models. We find that perceived behavioural control, subjective norms and attitudes (as part of the Theory of Planned Behaviour) are significant predictors of intent to reduce consumption. However, perceived behavioural control was not statistically significantly associated with behaviour. Environmental concern had a more significant influence on attitudes towards energy reduction than bill consciousness. Attitude was additionally significantly associated with fear of losing comfort and energy knowledge. Moreover, personal moral norms (as part of Value Belief Norm Theory) and willingness (as part of the Prototype Willingness Model) contributed to explaining the intent to reduce consumption, while willingness was also associated with behaviour.

Prosumers with photovoltaic systems can reduce their electricity expenses by increasing their consumption of self-generated electricity. This makes them more resilient to price shocks, like the 2022 European energy crisis. We evaluate how prosumers adapt their consumption behavior in response to such political uncertainty and increasing electricity prices. The collected survey and smart meter data allow us to evaluate the perceived self-reported and measured impact on self-consumption.Saving intentions due to the energy crisis are more clearly displayed by the survey than by the measured self-consumption. While solar radiation predominantly explains self-consumption changes, Google searches on electricity-related topics have limited explanatory power. However, considering time lags and the interaction with solar radiation leads to more nuanced insights on the effect of Google searches. Depending on the level of solar radiation, the effect of Google searches ranges from decreasing the daily self-consumption by 26.45 Wh to increasing it by 69.45 Wh.

In this paper, we report evidence collected in the context of the Horizon 2020 NUDGE project about the effectiveness of digital tools such as smartphone apps and web portals to realize nudging interventions towards different energy efficiency goals: from the reduction of heating energy and electricity to the increase of self-consumption in energy prosumer households. We analyse recorded events from the interaction of participants with those tools in the context of three different pilot experiments. We first assess the level of end user engagement with the apps and the portal, counting the number of distinct days that they interact with them. We find it to be highly heterogeneous, with up to 25% of participants in the Greek pilot and 12% in the Portuguese pilot not using the mobile app at all, and the rest forming three distinct groups of low, medium and high engagement. The interaction with the apps almost always lasts fractions of a minute and involves accessing a few app screens. We next turn to the actual users’ exposure to the nudging features of the digital tools to find out that high percentages of users (up to 50%) exhibit zero or very occasional exposure to the app screens that implement nudges. The mobile app users, in particular, can be grouped into four clusters depending on the level of engagement with the app and their exposure to its nudging features. Disappointingly, more than half the pilot participants belong to the cluster combining low engagement with low exposure to nudging. Combining these data with self-statements of participants in post-intervention surveys, we find no significant correlation between the level of nudging exposure and the (self-stated) motivation/ intentions to save energy.and communication emerged as one of the most prominent factors affecting EERs' acceptance. The paper analysed the online information sources based on readability, credibility, and interactivity, focusing on accessibility and the ability to generate tailor-made suggestions. The analysis revealed that the online information sources are disorganised and dispersed. The online platforms rarely provide information on prior case studies and more on financial subsidies, guidelines, and EER benefits. Lastly, we discussed the main barriers and potential solutions for these challenges.

Content caching has been experiencing revived interest within the context of current and next generation wireless networks. It brings content closer to users, decreasing the aggregate delivery costs and service delays for network providers. Recommender systems have become integral components of content provision sites. They offer personalized recommendations, increasing the individual user satisfaction and engagement with the content provider’s platform. Traditionally, caching and recommendation decisions are taken separately. However, there is a recent persistent trend where both network and content providers tend to deploy their own content delivery solutions. In light of this, we explore how the phenomenally conflicting objectives of content caching and recommendation can be jointly addressed. In this chapter we approach recommender systems as network traffic engineering tools that actively shape content demand to serve both user- and network-centric performance objectives. We introduce a model that captures the coupling between caching decisions and issued recommendations. Based on experimental evidence, we describe the impact of recommendations on user content requests and present a systematic way of engineering the user recommendations. Our viewpoint to recommender systems raises some concerns, not least ethical ones. Hence, we introduce a measure called preference distortion tolerance to quantify how much the engineered recommendations distort the original user content preferences. We describe the “recommendation window” as a way to controllably bound the distortion that recommendations will undergo and discuss specific properties that the recommender system should have in order to ensure that the “Quality of Recommendations (QoR)" is kept high by the issued recommendations. We formulate a joint optimization problem for the content to cache and recommend to each user, aiming to maximize the cache hit ratio. The preference distortion tolerance is embedded as a constraint to this problem formulation and marks an equally important dimension for assessing possible solutions. We prove that the problem lacks those properties that would guarantee its approximability, and devise a low-complexity practical algorithm that solves it efficiently. The algorithm is essentially a form of lightweight control over user recommendations, so that the recommended content is both appealing to the end-user and more friendly to the caching system and the network resources. We thoroughly evaluate it, by establishing its main properties and analytical performance bounds. We conduct an extensive sensitivity analysis over various system parameters, both analytically and through simulations with real and synthetic dataset. Next we extend our model and approach for a system in which users can access content from multiple caches, and analyse the intermediate joint caching and user association problem. We then discuss open directions and provide a detailed taxonomy of the related work. Due to space limitations, we do not provide proofs for our results. However, the interested reader may refer to [1-3] for more propositions and their detailed proofs.

Caching at the edge of the radio network is increasingly viewed as a promising countermeasure to the staggering demand for mobile video content. The persistent orientation of newer generations of mobile communication systems towards lower latency and faster radio access speeds only strengthens the arguments in its favor. When content caching is coordinated with other radio resource management functions, in particular, the benefits for the end users and the network operator are significant. In this paper, we investigate these benefits in cache-enabled small cell networks that jointly control (i) the Small-Cell Base Stations (SBSs) that serve as network access points for the mobile users; and (ii) the content that is stored at the SBS co-located caches. Our main contribution is a fast-converging computationally simple heuristic algorithm that iterates between assigning users to small cells and content to SBS caches, to maximize the overall cache hit ratio. The algorithm solutions compete with the optimal assignments at small problem instances and outperform alternative solutions for larger instances, especially when the content demand exhibits spatial locality. Combining good performance with non-prohibitive complexity, the algorithm could become a valuable tool for small cell network operators seeking to optimize the use of radio network resources.

The ever-growing trend of mobile users to consume high quality videos in their devices has pushed the backhaul network to its limits. Caching at Small-cell Base Stations (SBSs) has been established as an effective mechanism for alleviating this burden. Next generation mobile networks promise high Access Network density, hence multiple SBS association options per mobile user may exist. In this paper, we study the joint problem of mobile user association to SBSs and content placement to SBS-collocated caches, aiming to further optimize the utilization of backhaul network. The problem is solved periodically, considering time intervals where the users' location to the system is assumed to be fixed. First, we decompose the joint problem into two sub-problems that are solve sequentially, namely the content preference similarity-driven user association and the demand aware content placement sub-problems. We then propose a heuristic that consists of multiple phases. In particular, at a preparation phase, it performs clustering of users based on the similarity of their content preferences, accounting also for geographical constraints. The resulting clusters are then utilized for the demand-aware association of users to the SBS, while the placement of content is driven by the resulting local demand in each SBS, and takes place at the end. We demonstrate the effectiveness of our heuristic by evaluating its performance against multiple schemes that either lack a preparation phase or do not account for geographical constraints. As it is evident through the numerical results, the user clustering that takes place during the preparation phase can increase the overall cache hit ratio up to 20%.

In this paper, we investigate the performance gains that are achievable when jointly controlling (i) in which Small-cell Base Stations (SBSs) mobile users are associated to, (ii) which content items are stored at SBS co-located caches and (iii) which content items are recommended to the mobile users who are associated to different SBSs. We first establish a framework for the joint user association, content caching and recommendations problem, by specifying a set of necessary conditions for all three component functions of the system. Then, we provide a concrete formulation of the joint problem when the objective is to maximize the total hit ratio over all caches. We analyze the problems that emerge as special cases of the joint problem, when one of the three functions is carried out independently, and use them to characterize its complexity. Finally, we propose a heuristic that tackles the joint problem. Proof-of-concept simulations demonstrate that even this simple heuristic outperforms an optimal algorithm that takes only caching and recommendation decisions into account and provide evidence of the achievable performance gains when decisions over all three functions are jointly optimized.

Caching decisions typically seek to cache content that satisfies the maximum possible demand aggregated over all users. Recommendation systems, on the contrary, focus on individual users and recommend to them appealing content in order to elicit further content consumption. In our paper, we explore how these, phenomenally conflicting, objectives can be jointly addressed. First, we formulate an optimization problem for the joint caching and recommendation decisions, aiming to maximize the cache hit ratio under minimal controllable distortion of the inherent user content preferences by the issued recommendations. Then, we prove that the problem is NP-complete and that its objective function lacks those monotonicity and submodularity properties that would guarantee its approximability. Hence, we proceed to introduce a simpler heuristic algorithm that essentially serves as a form of lightweight control over recommendations so that they are both appealing to end-users and friendly to network resources. Finally, we draw on both analysis and simulations with real and synthetic datasets to evaluate the performance of the algorithm. We point out its fundamental properties, provide bounds for the achieved cache hit ratio, and study its sensitivity to its own as well as system-level parameters.