Abstract
Data quality plays a crucial role in the context of smart buildings. Meanwhile, missing data is relatively common in acquired datasets from sensors within the smart buildings. Poor data could result in a big bias in forecasting, control and operational services. Despite the common techniques to handle missing data, it is essential to systematically select the most appropriate approach for such missing values. This paper aims to focus on the lift systems as one of the essential parts in the smart buildings by exploring the most appropriate data imputation methods to handle missing data and to provide its service and allow a better understanding of patterns to issue the correct control actions based on forecasted models. The imputed data is not only investigated statistically but also modelled through machine learning algorithm to explore the impact of selecting inappropriate imputation techniques. Seven imputation techniques deployed on datasets with three level of missing values including 10%, 20% and 30% and the performance of methods examined through the normalized root mean square error (NRMSE) approach. In addition, the interaction between imputation techniques and a machine learning algorithm, namely random forest were examined. Findings from this paper can be employed in identifying an appropriate imputation technique not only within the lift datasets, but smart building context.

Abstract
This paper presents a self-scheduling framework, using a risk-constrained optimization model for the home energy management system (HEMS), considering fixed, controllable, and interruptible loads, as a new contribution to earlier studies. The objectives are reducing the electricity bill and managing the risk of purchasing energy over on-peak hours and prosumer's discomfort index (DI) due to shifting load to undesired hours. In this regard, the problem formulation is represented as a mixed-integer linear programming (MILP) model. Afterward, the proposed HEMS is promoted to a conditional value-at-risk (CVaR) model. The prosumer is equipped with an energy storage system and a solar photovoltaic (PV) panel. A substantial fraction of the load demand is controllable, and there is an inverter-based heating, ventilation, and air conditioning (HVAC), where HVAC is modeled as a variable-capacity interruptible load. The optimal scheduling of the loads is supposed to be done by the proposed HEMS, and the time-of-use (TOU) mechanism is utilized, including three price steps over the day. The results, obtained from thoroughly simulating the problem using household data, validate the performance of the presented HEMS in mitigating the amount of the electricity bill, while keeping the discomfort index of the prosumer at a desired level.

Abstract
Demand response (DR) services have the potential to enable large penetration of renewable energy by adjusting load consumption, thus providing balancing support to the grid. The success of such load flexibility provided by industry, communities, or prosumers and its integration in electricity markets, will depend on a redesign and adaptation of the current interactions between participants. New challenges are, however, bound to appear with the large scale contribution of smaller assets to flexibility, including, among others, the dispatch coordination, the validation of delivery of the DR provision, and the corresponding settlement of contracts, while assuring secured data access among interested parties. In this study we applied distributed ledger (DLT)/blockchain technology to securely track DR provision, focusing on the validation aspect, assuring data integrity, origin, fast registry, and sharing within a permissioned system, between all relevant parties (including transmission system operators (TSOs), aggregators, distribution system operators (DSOs), balance responsible parties (BRP), and prosumers). We propose a framework for DR registry and implemented it as a proof of concept on Hyperledger Fabric, using real assets in a laboratory environment, in order to study its feasibility and performance. The lab set up includes a 450 kW energy storage system, scheduled to provide DR services, upon a system operator request and the corresponding validations and verifications are done, followed by the publication on a blockchain. Results show the end to end execution time remained below 1 s, when below 32 requests/sec. The smart contract memory utilization did not surpass 1% for both active and passive nodes and the peer CPU utilization, remained below 5% in all cases simulated (3, 10, and 28 nodes). Smart Contract CPU utilization remained stable, below 1% in all cases. The performance of the implementation showed scalable results, which enables real world adoption of DLT in supporting the development of flexibility markets, with the advantages of blockchain technology.

Abstract
—The main objective of this work is to summarize the most commonly used dielectric fluids in the power distribution transformers, as well as to discuss what are the latest and the rationale behind those trends. The favorable and unfavorable reasons for any choice behind each of those dielectric fluids will be discussed. Additionally, this work also advances the power distribution transformers health index most commonly used to assess the condition of the transformer.

Abstract
Recently, indoor swimming pool activities have increased to promote health-enhancing physical activities, which require establishing suitable protocols for disinfection and water quality control. Normally, the assessment of the microbial quality of the water in the pools only considers the presence of different bacteria. However, other less frequent but more resistant pathogens, such as free-living amoebas (FLA), are not contemplated in both existing recommendation and research activities. FLA represent a relevant human health risk, not only due to their pathogenicity but also due to the ability to act as vehicles of other pathogens, such as bacteria. Therefore, this work aimed to study the physicochemical characteristics and the occurrence of potentially pathogenic FLA and bacteria in water samples from 20 public indoor swimming facilities in Northern Portugal. Our results showed that some swimming pools presented levels of pH, free chlorine, and conductivity out of the recommended limits. Pathogenic FLA species were detected in two of the facilities under study, where we also report the presence of both, FLA and pathogenic bacteria. Our findings evidence the need to assess the occurrence of FLA and their existence in the same environmental niche as pathogenic bacteria in swimming pool facilities worldwide and to establish recommendations to safeguard the health of the users.

Abstract
While it is generally accepted that our fossil fuel-dominated energy systems must undergo a sustainable transition, researchers have often neglected the potential impacts of this on water and land systems. However, if unintended environmental impacts from this process are to be avoided, understanding its implications for land use and water demand is of crucial importance. Moreover, developed countries may induce environmental stress beyond their own borders, for instance through extensive imports of bioenergy. In this paper, Germany serves as an example of a developed country with ambitious energy transformation targets. Results show that in particular, the politically-driven aspiration for more organic farming in Germany results in a higher import quota of biomass, especially biofuels. These imports translate into land demand, which will exceed the area available in Germany for bioenergy by a factor of 3-6.5 by 2050. As this will likely bring about land stress in the respective exporting countries, this effect of the German energy transformation ought to be limited as much as possible. In contrast, domestic water demand for the German energy system is expected to decrease by over 80% through 2050 due to declining numbers of fossil-fuelled power plants. However, possible future irrigation needs for bioenergy may reduce or even counterbalance this decreasing effect. In addition, energy policy targets specific to the transport sector show a high sensitivity to biomass imports. In particular, the sector-specific target for greenhouse gas reductions will seemingly promote biomass imports, leading to the above-described challenges in the pursuit of sustainability.