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The residential sectorʼs substantial electricity consumption, driven by heating demands during winter, necessitates optimal energy consumption strategies in the era of decarbonization. To address this challenge, this paper introduces a synthetic dataset specifically tailored to simulate energy consumption in residential apartment buildings. Focusing on the interplay of cold weather conditions and the effects of aging factors, the dataset comprehensively encompasses key variables, including indoor temperature, energy consumption, outdoor temperature, outdoor humidity and solar radiation. It underscores the considerable impact of building aging on energy consumption patterns. The datasetʼs significance extends across various domains, particularly in the realms of energy forecasting and thermal modelling. It serves as a robust foundation for predicting future consumption patterns, optimizing resource allocation, and refining energy efficiency strategies. The inclusion of indoor temperature data facilitates an in-depth thermal modelling approach, shedding light on intricate relationships that influence building performance in cold climates. Beyond traditional, the dataset proves invaluable in nonlinear modelling and machine learning. It emerges as a key tool for algorithm training, enhancing forecast precision, and supporting well-informed decision-making. The introduction of a temporal dimension by accounting for aging factors allows for the exploration of evolving building components over time, a critical consideration for sustainable energy management and building maintenance strategies. The dataset was meticulously generated by creating geometry using SketchUp and conducting energy modelling and simulations via the OpenStudio platform, which integrates the Energy Plus modelling engine to enhance accuracy. In summary, this synthetic dataset generation provides valuable insights into energy consumption in residential buildings exposed to cold weather conditions and the influences of aging. Its multifaceted applications across forecasting, modelling, management, and planning underscore its potential to advance sustainable and efficient energy practices.

In flexibility markets, aggregators serve as crucial intermediaries by consolidating and selling consumer flexibility to grid operators or distribution system operators (DSOs). They are essential for grid management, offering load reductions based on power limits, and estimating expected consumer load in demand response scenarios. However, the inherent uncertainty in consumer behaviour poses a significant challenge, leading to deviations between projected and actual power consumption. In this context, this paper proposes a methodology for quantifying forecast uncertainties in power profiles at the aggregator level. The proposed methodology introduces a model-based approach to provide a more comprehensive representation of uncertainty and investigation of load variations. It provides load forecast values as comprehensive distributions, which are then sampled to generate newly sampled data from which the probability density function is extracted to quantify uncertainty, expressed by confidence intervals around the expected output. This approach aids in identifying the flexibility requirements for aggregated household power consumption, assists in quantifying uncertainties, and determines the flexibility needed for accurate forecasts of such consumption, which is essential for informed decision-making. The effectiveness of the proposed strategy is demonstrated using a synthetic dataset to assess its capability to quantify uncertainties in probabilistic forecasts. Additionally, a potential case study with a neighborhood of 14 houses connected to the same distribution transformer is presented to validate the proposed method. A comparative investigation of quantified uncertainties is presented by employing the Additive Gaussian Process (AGP), the Prophet forecasting, and the quantile regression, highlighting the usefulness of the proposed approach in flexibility markets. The results demonstrated the superiority of AGP-based load forecasts and flexibility needs with precise prediction accuracy. The comparative study demonstrates that the proposed method with AGP presents a minimum uncertainty when forecasting the total residential load than other benchmark models with a percentage of 26% and 21% in mean absolute error, respectively, for the different datasets. The continuous ranked probability score also revealed a 39% increase in the accuracy of probabilistic forecasts via the proposed method in contrast to others.

Thermal runaway (TR) of lithium-ion batteries has always been a topic of concern, and the safety of batteries is closely related to the operating temperature. An overheated battery can significantly impact the surrounding batteries, increasing the risk of fire and explosion. To improve the safety of battery modules and prevent TR, we focus on the characteristics of temperature distribution and thermal spread of battery modules under overheating conditions. The heat transfer characteristics of battery modules under different battery thermal management systems (BTMSs) are assessed. In addition, the effects of abnormal heat generation rate, abnormal heat generation location, and ambient temperature on the temperature distribution and thermal spread of battery modules are also studied. The results indicate that the BTMS consisting of flat heat pipes (FHPs) and bottom and side liquid cooling plates can effectively suppress thermal spread and improve the safety of the battery module.

The effectiveness of regenerative braking strategies plays an important role in extending the driving range of electric vehicles. Since the driver is still an essential factor in levels 3 and 4 of intelligent electric vehicles, improving user acceptance and adoption of the braking control strategy is crucial. This paper puts forward a new regenerative braking strategy to find a compromise between optimal braking control performance and naturalistic regeneration performance while satisfying the maximum speed preference when driving between two-stop events. Unlike other similar works that only maximize regenerative braking energy while satisfying the physical limits of an electrified powertrain, this paper considers naturalistic regeneration performance. To achieve this, firstly, the power regenerated by three drivers is predicted with a long-horizon (30 seconds), using long-short-term memory networks (LSTM) and non-linear autoregressive exogenous model (NARX). Subsequently, an estimation of the energy recovery maximization rate is performed to give a perception of the naturalistic regeneration performance. As this performance varies, the deceleration planning employs three horizon scales of long, medium, and short, determined by the energy recovery maximization rate. Finally, dynamic programming (DP) is utilized to optimize a deceleration profile. The study utilizes real data of inverter efficiency, transmission efficiency, and motor-to-battery efficiency map. The outcome of this study shows that the proposed regeneration braking strategy is adaptive, improving regeneration efficiency by 39,6% for driver 1, 16% for driver 2, and 26% for driver 3, and forecasting the optimality of some deceleration behaviors.

Demand response and distributed energy storage play a crucial role in improving the efficiency and reliability of electric grids. This article describes a strategy for optimally integrating distributed energy storage units within a forward market to address space heating demand under a Stackelberg game in isolated microgrids. The proposed strategy performs distributed management in an offline fashion through proximal decomposition methods. It leverages stochastic programming to consider user flexibility degree and wind power generation uncertainties. Also, flexibility for demand response is realized through electric thermal storage (ETS). The performance of the proposed strategy is evaluated via simulation studies carried out through a case study in Kuujjuaq, Quebec. Ten residential agents compose the demand side, each with flexibility levels and economic preferences. The simulation results show that adapting ETS results in economic savings for the customers. Those benefits increased in the presence of wind power, from 25% to 40% on average. Likewise, coordinated strategies led the coordinator to obtain reduced operational costs and peak-to-average ratio by over 35% and 56%, respectively. The proposed approach reveals that optimal coordination of ETS in the presence of dynamic tariffs can reduce diesel consumption, maximize renewable production and reduce grid stress.

For a sustainable operation of multiple Self-Guided Vehicles (SGVs) in a dynamic manufacturing environment, it is essential to guarantee collision-free and efficient navigation to the autonomous mobile platforms and safety to the surrounding subjects. To prevent from navigation failures, an SGV must avoid conflicts that constrain itself to abruptly brake or stop to avoid collisions. These inefficient conflicts result from unexpected changes in the configuration space or due to nearby unforeseen obstacle. In this paper, a navigation approach is proposed to adapt the global trajectory in order to reduce conflict occurrence while limiting energy consumption of the mobile platform. To generate such trajectory, first the collision risks are characterized using an objective risk perception parameter, the Time-To-Collision TTC, that rely on the kinematics of the egoSGV and the neighboring obstacles. Next, weighted Kernel Density Estimation (wKDE) defines the spatial distribution of conflict severity in configuration space. The defined zones are incorporated as a conflict layer in the global map. Then, a global trajectory planner algorithm is used to weigh between the length cost and conflict cost. Finally, to test the proposed solution, a simulation is performed in a factory-like environment, then an experiment is conducted with a real SGV. In comparison with the state-of-the-art geometrical path planning method, the results show that the proposed approach reduces navigation failures by up to 52%, while reducing the trajectory execution time by around up to 10 %. Also, the smoothness of the executed motion allowed to reduce energy consumption by over 12%.

Abstract As an essential feature of autonomous road vehicles, obstacle detection must be executed on a real‐time onboard platform with high accuracy. Cameras are still the most commonly used sensors in autonomous driving. Most detections using cameras are based on convolutional neural networks. In this regard, a recent teacher–student approach, called transfer learning, has been used to improve the neural network training process. This approach has only been used with a neural network acting as a teacher to the best of our knowledge. This paper proposes a novel way of improving training data based on attention transfer by getting the attention map from a human. The proposed method allows the dataset size reduction by 50%, which leads to up to a 60% decline in the training time. The experimental results indicate that the proposed method can enhance the F1‐score of the network by up to 10% in winter conditions.

Flexibility from demand-side resources is increasingly required in modern power systems to maintain the dynamic balance between demand and supply. This flexibility comes from elastic users managing controllable loads. In this context, controlling Electric Space Heaters (ESHs) is of particular interest because it can leverage building inner thermal storage capacity to shift consumption while maintaining comfort conditions. Some economic Demand Response (DR) programs have considered exploiting EHSs flexibility potentials in recent years. However, these programs still struggle to engage customers due to the complexity of processing price signals for inexpert users. Therefore, it is necessary to develop automated tools for helping users to operate their loads. Accordingly, this paper presents a recommender system based on Gaussian processes to discover users’ valuations of thermal comfort and perform the predictive control of their ESHs. The proposed method enables customers to participate in DR programs and impose their preferences through straightforward queries instead of directly changing control parameters. Validation results demonstrate that users maximize their utility by supplying noiseless and consistent data to the recommender system. Additionally, the suggested approach achieves a higher acceptance rate than other methods from the literature, such as persistency and support vector machines.

Spot markets provide an interesting opportunity for profit maximization by energy trading based on immediate decisions on participant bids. However, their short market-clearing time can affect computational efficiency, search space, and reliability of price-energy allocation to bidding participants. Accordingly, developing a prompt and effective decision-making process plays a vital role in smooth energy delivery in these markets. This paper proposes an approach to alleviate the computational cost of the spot market aggregator in order to decide price-energy bids. The proposed bidding model is developed for the transactive energy systems, where the spot market aggregator utilizes the proposed method to maximize profit by choosing participants’ demand-side bids. The proposed method can efficiently manage participants’ combined energy and price information and avoid a highly complicated search space. It takes advantage of the multi-variable Taylor series approximation to create users’ individual cost functions. The approximated cost functions lead to user-specific bids that expedite the spot market transaction while maintaining aggregator profit. The resultant system is able to exercise profit maximization with high performance within milliseconds. The efficiency of this scheme is also demonstrated through a comparative study by using the particle swarm optimization method.

Order picker routing refers to the process of collecting a set of products with the minimum travel time. Recently, a new generation of Automated Guided Vehicles (AGVs) has been developed to assist human order pickers in order to minimize their travel time. These vehicles are using battery as energy source. However, the routing energy efficiency aspect of these systems remains unexplored. Yet any improvement in power consumption will ultimately reduce the DOD (depth of discharge) of the battery and increase its lifespan. For example, in many real AGV applications incorporating the effect of load mass has been neglected, although its importance. In most studies, the methodology proposed for the order picking routing problem does not allow neither the integration of the mass of each Stock Keeping Unit (SKU) nor the calculation of associated energy costs. Those studies are generally limited to ensure that all the items requested by an order are picked up with minimum travel time/distance. In this paper, an Energy Efficient Order Picking Routing algorithm named EE-OPR is proposed to realize an efficient AGV tour with an acceptable trade-off between energy preservation and travel time minimization. The proposed approach takes into account the mass of loads and its accumulation throughout the pick tour since it intensifies the rolling resistance losses on flat ground, especially at lower speeds. In this regard, an optimization method by means of dynamic states graph is developed. This method is applied to different warehouse layouts. The performance of the suggested algorithm is evaluated by comparing it with an approach minimizing only travel time consumption. Results show that the optimized tours, offered by EE-OPR are effective and robust, with an 18% average saving on the total cost of picking tour.

Demand Response (DR) programs show great promise for energy saving and load profile flattening. They bring about an opportunity for indirect control of end-users’ demand based on different price policies. However, the difficulty in characterizing the price-responsive behavior of customers is a significant challenge towards an optimal selection of these policies. This paper proposes a Demand Response Aggregator (DRA) for transactive policy generation by combining a Reinforcement Learning (RL) technique on the aggregator side with a convex optimization problem on the customer side. The proposed DRA can maintain users’ privacy by exploiting the DR as the only source of information. In addition, it can avoid mistakenly penalizing users by offering price discounts as an incentive to realize a satisfying multi-agent environment. With an ensured convergence, the resultant DRA is capable of learning adaptive Time-of-Use (ToU) tariffs and generating near-to-optimal price policies. Moreover, this study suggests an off-line training procedure that can deal with issues related to the convergence time of RL algorithms. The suggested process can notably expedite the DRA convergence and, in turn, enable online applications. The developed method is applied to a set of residential agents in order to benefit them by regulating their thermal loads according to generated price policies. The efficiency of the proposed approach is thoroughly evaluated from the standpoint of the aggregator and customers in terms of load shifting and comfort maintenance, respectively. Besides, the superior performance of the selected RL method is represented through a comparative study. An additional assessment is also conducted by use of a coordination algorithm to validate the competitiveness of the recommended DR program. The multifaceted evaluation demonstrates that the designed scheme can significantly improve the quality of the aggregated load profile with a low reduction in the aggregator’s income.

The increase of Plug-in Electric Vehicles (PEVs) penetration in distribution systems necessitates processing strategic assets in order to deal with their energy needs. A careful investigation into matters related to PEV charging management under actual circumstances can be regarded as the critical step towards enabling this process. Accordingly, this paper intends to design a practical controller capable of performing charging scheduling under uncertainties related to the lack of access to crucial PEV information accounting for departure time, energy requirement, and power demand nonlinearity. Although such an issue can be encountered when developing charging models for real-world conditions, it has not been adequately taken into consideration. The proposed controller carries out charging scheduling through a procedure with a set of effective straightforward algorithms, essential for actual applications. Particularly, it takes advantage of a Bayesian forecasting model that is able to efficiently predict charging energy demand according to car owner’s behavior. In addition, it employs a stochastic optimization framework to schedule PEV charging based on the dynamic electricity price and user preference. Several case studies are conducted to examine the performance of suggested controller in optimal scheduling by exploiting real data. The evaluation process is executed through a comparative analysis by using a deterministic method, as the ideal case, which exploits a full-information space. The results show that the proposed procedure can offer competitive charging schedules, which can minimize the cost while satisfying user desires. The designed controller can successfully manage PEV charging in the presence of stochastic phenomena with limited information access, and thus, enable physical implementations.

For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the technical characteristics of case studies. Accordingly, several databases, mainly from Europe and the US, have been publicly released to enable basic research to address NILM issues raised by their challenging features. Nevertheless, the resultant enhancements are limited to the properties of these datasets. Such a restriction has caused NILM studies to overlook residential scenarios related to geographically-specific regions and existent practices to face unexplored situations. This paper presents applied research on NILM in Quebec residences to reveal its barriers to feasible implementations. It commences with a concise discussion about a successful NILM idea to highlight its essential requirements. Afterward, it provides a comparative statistical analysis to represent the specificity of the case study by exploiting real data. Subsequently, this study proposes a combinatory approach to load identification that utilizes the promise of sub-meter smart technologies and integrates the intrusive aspect of load monitoring with the non-intrusive one to alleviate NILM difficulties in Quebec residences. A load disaggregation technique is suggested to manifest these complications based on supervised and unsupervised machine learning designs. The former is aimed at extracting overall heating demand from the aggregate one while the latter is designed for disaggregating the residual load. The results demonstrate that geographically-dependent cases create electricity consumption scenarios that can deteriorate the performance of existing NILM methods. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly in Quebec houses.

High penetration of selfish Home Energy Management Systems (HEMSs) causes adverse effects such as rebound peaks, instabilities, and contingencies in different regions of distribution grid. To avoid these effects and relieve power grid stress, the concept of HEMSs coordination has been suggested. Particularly, this concept can be employed to fulfill important grid objectives in neighborhood areas such as flattening aggregated load profile, decreasing electricity bills, facilitating energy trading, diminishing reverse power flow, managing distributed energy resources, and modifying consumers' consumption/generation patterns. This paper reviews the latest investigations into coordinated HEMSs. The required steps to implement these systems, accounting for coordination topologies and techniques, are thoroughly explored. This exploration is mainly reported through classifying coordination approaches according to their utilization of decomposition algorithms. Furthermore, major features, advantages, and disadvantages of the methods are examined. Specifically, coordination process characteristics, its mathematical issues and essential prerequisites, as well as players concerns are analyzed. Subsequently, specific applications of coordination designs are discussed and categorized. Through a comprehensive investigation, this work elaborates significant remarks on critical gaps in existing studies toward a useful coordination structure for practical HEMSs implementations. Unlike other reviews, the present survey focuses on effective frameworks to determine future opportunities that make the concept of coordinated HEMSs feasible. Indeed, providing effective studies on HEMSs coordination concept is beneficial to both consumers and service providers since as reported, these systems can lead to 5% to 30% reduction in electricity bills.

Due to the impact of human lifestyle on building energy consumption, the development of occupants' behavior models is crucial for energy-saving purposes. In this regard, occupancy modeling is an effective approach to intend such a purpose. However, the literature reveals that existing occupancy models have limitations related to the representation of occupancy state duration and the integration of occupancy variability among individuals. Accordingly, this paper proposes an explicit differentiated duration probabilistic model to generate realistic daily occupancy profiles in residential buildings. The discrete-time Markov chain theory and the semi-parametric Cox proportional hazards model (Cox regression) are used to predict household occupancy profiles. The proposed model is able to capture occupancy states duration and integrate human behavior variability according to individuals' characteristics. Moreover, a parametric analysis is employed to investigate these characteristics' impact on the model performance and consequently, select the most significant input variables. A validation process is conducted by comparing the model performance with that of previous methods, presented in the literature. For this purpose, the k crossvalidation technique is utilized. Validation results demonstrate that the proposed approach is highly efficient in generating realistic household occupancy profiles.

Human preferences and lifestyles significantly impact buildings’ energy consumption. Consequently, a better understanding of occupants’ behavior is crucial to decrease energy consumption and maintain occupants’ comfort. Occupant-centric control (OCC) strategies are effective approaches to fulfil such a purpose. As such, occupancy detection and prediction are of prime importance, particularly to manage Electric Space Heating (ESH) systems, due to the relatively slow dynamics of the temperature in dwellings. This paper proposes an Explicit Duration Hidden Markov Model (EDHMM) for unsupervised online presence detection and a hazard-based approach for occupancy prediction. Moreover, a control strategy using a cost function, weighted by occupancy predictions, and a load-shifting strategy based on time-varying electricity price are put forward. This work initially validates the consistency of the proposed approach by using synthetic data generated by a Monte Carlo simulation. Subsequently, the performance of our framework is compared with previous methods presented in the literature through experimental validation. Results demonstrate that the proposed EDHMM approach is efficient in detecting occupancy states. Besides, the results of the field implementation show the potential of the proposed control strategy to preserve occupants’ thermal comfort while decreasing the heating energy consumption.

Anomaly detection is a significant application of residential appliances load monitoring systems. As an essential prerequisite of load diagnosis services, anomaly detection is critical to energy saving and occupant comfort actualization. Notwithstanding, the investigation into diagnosis of household anomalous appliances has not been decently taken into consideration. This paper presents an extensive study about operation-time anomaly detection of household devices particularly, refrigerators, in terms of appliances candidate, by utilizing their energy consumption data. Energy as a quantitative property of electrical loads, is a reliable information for a robust diagnosis. Additionally, it is very practical since it is low-priced to measure and definite to interpret. Subsequently, an on-line anomaly detection approach is proposed to effectively determine the anomalous operation of the household appliances candidate. The proposed approach is capable of continuously monitoring energy consumption and providing dynamic information for anomaly detection algorithms. A machine learning-based technique is employed to construct efficient models of appliances normal behavior with application to operation-time anomaly detection. The performance of the suggested approach is evaluated through a set of diagnostic tests, by utilizing normal and anomalous data of targeted devices, measured by an acquisition system. In addition, a comparison analysis is provided in order to further examine the effectiveness of the developed mechanism by exploiting a public database. Moreover, this study elaborates sensible remarks on an effective management of anomaly detection and diagnosis decision phases, pivotal to correctly recognition of a faulty/abnormal operation. Indeed, through experimental results of case studies, this work assists in the development of a load monitoring and anomaly detection system with practical implementation.

Proton exchange membrane fuel cell (PEMFC) can be a significant eco-friendly alternative power source for vehicles. However, under subfreezing conditions, cell degradation and irreversible performance decay can occur because of ice formation and repetitive thaw/freeze cycles. These problems have limited the further commercialization of PEMFC in cold weather countries. Thus, many improvements have been made to repair the freeze protection and rapid cold startup problems in PEMFC vehicles. In this paper, a comprehensive review dedicated to engineers of the recent research progress on the PEMFC cold start problems is presented. Systems and methods for fuel cell shutdown are summarized and classified into two categories: purge solution and material to avoid freezing. Regarding the system and solutions for PEMFC cold startup, different heating solutions are classified into two main groups depending on their heating sources and categorized as internal and external heating methods. This paper concludes with a detailed review of cold startup strategies based on an exhaustive survey of journal papers and patents.

Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

An efficient participation of prosumers in power system management depends on the quality of information they can obtain. Prosumers actions can be performed by automated agents that are operating in time-changing environments. Therefore, it is essential for them to deal with data stream problems in order to make reliable decisions based on the most accurate information. This paper provides an in-depth investigation of data and concept drift issues in accordance with residential prosumer agents. Additionally, the adaptation techniques, forgetting mechanisms, and learning strategies employed to handle these issues are explored. Accordingly, an approach is proposed to adapt the prosumer agent models to overcome the gradual and sudden concept drift concurrently. The suggested method is based on triggered adaptation techniques and performance-based forgetting mechanism. The results obtained in this study demonstrate that the proposed approach is capable of constructing efficient prosumer agents models with regard to the concept drift problem.