Laurent Delahoche

Accurate forecasting of indoor environmental variables is essential for occupancy-aware building energy management, yet the interplay between neural network architecture, forecasting strategy, and downstream occupancy inference remains poorly understood. This study presents a comprehensive controlled evaluation framework for long short-term memory (LSTM)-based multihorizon indoor CO ₂ forecasting and downstream occupancy-proxy inference using two years of residential environmental sensor data. Established forecasting approaches are evaluated across six forecast horizons from 1 h to 48 h. A crossed strategy-by-hyperparameter experiment descriptively partitions observed mean absolute error (MAE) variation: hyperparameter configuration accounts for 80.7% at [Formula: see text] h, whereas strategy accounts for 39.1% at [Formula: see text] h, the larger of the two main-effect shares at that horizon. Temporal feature augmentation reduces MAE by 9.3% at [Formula: see text] h and 8.5% at [Formula: see text] h, while increasing MAE at [Formula: see text] h. Finally, we propose an unsupervised occupancy inference framework applying absolute threshold, rate-of-change, and adaptive threshold methods to both measured and LSTM-forecasted CO ₂ . These findings provide operational guidance for deploying neural network forecasters in intelligent building management systems.

Even though Electric Powered Wheelchairs (EPWs) are a useful tool for meeting the needs of people with disabilities, some disabled people find it difficult to use regular EPWs that are joystick-controlled. Smart wheelchairs that use Brain–Computer Interface (BCI) technology present an efficient solution to this problem. This article presents a cutting-edge intelligent control wheelchair that is intended to improve user involvement and security. The suggested method combines facial expression analysis via a camera with EEG signal processing using the EMOTIV Insight EEG dataset. The system generates control commands by identifying specific EEG patterns linked to facial expressions such as eye blinking, winking left and right, and smiling. Simultaneously, the system uses computer vision algorithms and inertial measurements to analyze gaze direction in order to establish the user’s intended steering. The outcomes of the experiments prove that the proposed system is reliable and efficient in meeting the various requirements of people, presenting a positive development in the field of smart wheelchair technology.

The energy consumption of a building is significantly influenced by the habits of its occupants. These habits not only pertain to occupancy states, such as presence or absence, but also extend to more detailed aspects of occupant behavior. To accurately capture this information, it is essential to use tools that can monitor occupant habits without altering them. Invasive methods such as body sensors or cameras could potentially disrupt the natural habits of the occupants. In our study, we primarily focus on occupancy states as a representation of occupant habits. We have created a model based on artificial neural networks (ANNs) to ascertain the occupancy state of a building using environmental data such as CO₂ concentration and noise level. These data are collected through non-intrusive sensors. Our approach involves rule-based a priori labeling and the use of a long short-term memory (LSTM) network for predictive purposes. The model is designed to predict four distinct states in a residential building. Although we lack data on actual occupancy states, the model has shown promising results with an overall prediction accuracy ranging between 78% and 92%.

Horizontal collaboration is one of the most effective strategies applied to address issues related to sustainability and the adverse effects of commodity distribution. Although it has received increased attention in recent years, no synthesis has been conducted to present the main practices and sustainability indicators used to assess the performance of horizontal collaboration. In addition, the factors that can further improve the performance of horizontal collaboration in terms of sustainability have not been presented in any literature review. Thus, we expose, in this paper, a review of the existing studies dealing with horizontal collaboration, and we define the key indicators used to measure its performance. The most commonly used practices of horizontal collaboration are also illustrated. In fact, the main objectives of the present study are to highlight the impact of certain factors on the success of collaboration and to identify several guidelines for researchers and companies wishing to implement horizontal collaboration.

With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads turns out to be a key issue to avoid energy wastage and to build effective power management strategies. Furthermore, energy consumption information can be considered historical time series data that are required to extract all meaningful knowledge and then forecast the future consumption. In this work, we aim to model and to compare three different machine learning algorithms in making a time series power forecast. The proposed models are the Long Short-Term Memory (LSTM), the Gated Recurrent Unit (GRU) and the Drop-GRU. We are going to use the power consumption data as our time series dataset and make predictions accordingly. The LSTM neural network has been favored in this work to predict the future load consumption and prevent consumption peaks. To provide a comprehensive evaluation of this method, we have performed several experiments using real data power consumption in some French cities. Experimental results on various time horizons show that the LSTM model produces a better result than the GRU and the Drop-GRU forecasting methods. There are fewer prediction errors and its precision is finer. Therefore, these predictions based on the LSTM method will allow us to make decisions in advance and trigger load shedding in cases where consumption exceeds the authorized threshold. This will have a significant impact on planning the power quality and the maintenance of power equipment.

The benefits of horizontal collaboration depend heavily on the flexibility that each partner allows in his delivery conditions through the creation of better and more efficient distribution plans. On the other hand, the sharing of savings is one of the main factors that can block the success of collaboration and produce, consequently, conflicts between partners. This paper argues that to achieve equitability, the sharing of collaborative savings must take into consideration the partners’ participation in sustainability and flexibility. Involving each partner's sustainability and flexibility when sharing the gains from horizontal collaboration is an effective way to persuade companies to relax their plans. First, the importance of including these two factors on the performance of horizontal collaboration is highlighted. The introduced approach consists in using the decomposition method to determine a sustainability and flexibility rate for each partner. Then, an allocation approach based on cooperative game theory (namely τ-value), is developed. It combines each partner's impact on sustainability and his level of flexibility in terms of delivery time. The obtained results show that the suggested approach can adjust the results of the traditional approach by preventing the partners from benefiting from each other.

To face the new challenges caused by modern industry, logistics operations managers need to focus more on integrating sustainability goals, adapt to unexpected disruptions and find new strategies and models for logistics management. The COVID-19 pandemic has proven that unforeseen fragilities, negatively affecting the supply chain performance, can arise rapidly, and logistics systems may confront unprecedented vulnerabilities regarding network structure disruption and high demand fluctuations. The existing studies on a resilient logistics network design did not sufficiently consider sustainability aspects. In fact, they mainly addressed the independent planning of decision-making problems with economic objectives. To fill this research gap, this paper concentrates on the design of resilient and sustainable logistics networks under epidemic disruption and demand uncertainty. A two-stage stochastic mixed integer programming model is proposed to integrate key decisions of location–allocation, inventory and routing planning. Moreover, epidemic disruptions and demand uncertainty are incorporated through plausible scenarios using a Monte Carlo simulation. In addition, two resiliency strategies, namely, capacity augmentation and logistics collaboration, are included into the basic model in order to improve the resilience and the sustainability of a logistics chain network. Finally, numerical examples are presented to validate the proposed approach, evaluate the performance of the different design models and provide managerial insights. The obtained results show that the integration of two design strategies improves resilience and sustainability.