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 article presents a restoration approach for improving the resilience of electric distribution systems (EDSs) by taking advantage of several operational resources. In the proposed approach, the restoration process combines dynamic network reconfiguration, islanding operation of dispatchable distributed generation units, and the prepositioning and displacement of mobile emergency generation (MEG) units. The benefit of exploring a demand response (DR) program to improve the recoverability of the system is also taken into account. The proposed approach aims to separate the in-service and out-of-service parts of the system while maintaining the radiality of the grid. To assist the distribution system planner, the problem is formulated as a stochastic-scenario-based mixed-integer linear programming model, where uncertainties associated with PV-based generation and demand are captured. The objective function of the problem minimizes the amount of energy load shedding after a fault event as well as PV-based generating curtailment. To validate the proposed approach, adapted 33-bus and 83-bus EDSs are analyzed under different test conditions. Numerical results demonstrate the benefits of coordinating the dynamic network reconfiguration, the prepositioning and displacement of MEG units, and a DR program to improve the restoration process.

Abstract
The issue of a state estimation-based fault-tolerant controller for direct current (dc) microgrids (MGs) is studied in this article. It is considered that the dc MG contains nonlinear constant power load (CPL) and is subjected to actuator faults. Current sensors are not installed and the voltages of the dc MG are measured in the presence of noise and sensor faults. To estimate the system states, a novel dual-Extended Kalman filter is proposed, which simultaneously estimates the states and faults. The fault- and noise-free estimations are then deployed in a nonlinear Takagi-Sugeno fuzzy predictive controller to regulate the dc MG. The proposed method outperforms the exiting results, being robust against faults and noise. Also, the predictive scheme makes it robust against system uncertainties and forces the system states to converge the desired values, precisely. The accuracy and robustness of the developed method are evaluated and compared to advanced state-of-the-art techniques for a typical dc MG with a resistive load, CPL, and energy storage unit.

Abstract
In this paper, a multi-temporal optimal power flow (OPF) model for radial networks is proposed. The mathematical problem formulation is presented as a mixedinteger quadratically constrained programming (MIQCP) problem. The main core of the developed OPF problem is benefiting from the second-order conic programming (SOCP) approach while the quadratic constraints of the power flow equations have been efficiently handled. In the developed model, the dynamic behaviour of the electrical energy storage (EES) has been addressed for the day-ahead operation problem. In addition, the developed model is tested and verified for both normal and contingent events and the obtained results are satisfactory in terms of feasibility and optimality. In the islanded operation, a grid-forming unit is the main responsible for maintaining the voltage reference while other units behave as slave. The model is tested on the modified IEEE 33-bus network to verify the performance of the developed tool.

Abstract
Protection and control systems represent an essential part of distribution networks, ensuring the physical integrity of components and improving system reliability. Protection devices isolate a portion of the network affected by a fault, while control devices reduce the number of de-energized loads by transferring loads to neighboring feeders. The integration of distributed generation has the potential to improve the continuity of energy services through islanding operation during outage conditions. In this context, this paper presents a multi-objective optimization approach for the size and allocation of protection and control devices in distribution networks with microgrids supplied by renewable energy sources. Reclosers, fuses, remote-controlled switches, and directional relays are considered in the formulation. The demand and generation uncertainties define the islanding operation and the load transfer possibilities. A genetic algorithm is presented to solve the allocation problem. The compromise programming is performed to choose the best solution from the Pareto front. Results show interesting setups for the protection system and viability of islanding operation.

Abstract
This paper investigates prosumers' flexibility provision for the optimal operation of active distribution networks in a transactive energy (TE) market. From a prosumer point of view, flexibility can be provided to operators using renewable energy resources (RES) and demand response (DR) through home appliances with the ability to modify their consumption profiles. In the TE market model, the distribution system operator (DSO) is responsible for market-clearing mechanisms and controlling the net power exchange between the distribution network and the upstream grid. The contribution of this work is the enhancement of a strategy to reduce operational costs of an active distribution network by using prosumers' flexibility provision through an aggregator or a smart building coordinator. To this end, a TE market for both energy and flexibility trading at distribution networks is presented, demonstrating the possibility to fulfill DSO requirements through the flexibility contributions in the day-ahead (DA) and real-time (RT) markets.