Despite of its importance and potential, the research on the peak power forecasting has received little attention. The decrease of the peak power not only reduces operational expense, but also avoids outages especially during the peak demand season. Thus, peak power forecasting, which is the key enabler for such advantages, can bring significant gains especially to a large-scale, energy-intensive manufacturing plant. This paper proposes a high-precision multi-step forecasting method to predict both the the peak power series and time of day the peak occurs. The proposed approach first predicts the peak power for a certain timespan (e.g., a day) by solving a regression problem with multiple features, including daily workload and weather forecast. Then, it generates hourly peak power series for the same timespan to increase the prediction accuracy and to identify the peak hour. In contrast to the daily workload plans, hourly plans rarely exist in practice and thus, hourly peak power forecasting is an auto-regression problem which becomes challenging as prediction timespan increases. In this work, a Constrained and Conditional Transformer (C2Transformer) is proposed for accurate multi-step peak power forecasting. The proposed model takes in the past hourly peak power series of length k along with a single peak predicted over a timespan of length n . Conditioning on the predicted long-term peak, the model generates n hourly power peaks. Also, the proposed C2Transformer has an additional constraint which is minimizing the difference between the predicted peak among n hours and the maximum value among the generated hourly peaks. Through extensive evaluations on a real data set from multiple sources, the proposed C2Transformer has shown superior performance to the widely used deep learning models.

Vehicular communication is revolutionizing transportation by enhancing passenger experience and improving safety through seamless message exchanges with nearby vehicles and roadside units (RSUs). To accommodate the growing number of vehicles in dense urban traffic with limited channel availability, non-orthogonal multiple access (NOMA) is a promising solution due to its ability to improve spectral efficiency by sharing channels among multiple users. However, to completely leverage NOMA on mobile vehicular networks, a chain of operations and resources must be optimized, including vehicle user (VU) and RSU association, channel assignment, and optimal power control. In contrast, traditional orthogonal multiple access (OMA) allocates separate channels to users, simplifying management but falling short in high-density environments. Additionally, enabling NOMA-OMA switching can further enhance the system performance while significantly increasing the complexity of the optimization task. In this study, we propose an optimized framework to jointly utilize the power domain NOMA in a vehicular network, where dynamic NOMA-OMA switching is enabled, by integrating the optimization of vehicle-to-RSU association, channel assignment, NOMA-OMA switching, and transmit power allocation into a single solution. To handle the complexity of these operations, we also propose simplified formulations that make the solution practical for real-time applications. The proposed framework reduces total power consumption by up to $27\%$ compared to Util&LB/opt, improves fairness in user association by $18\%$, and operates efficiently with minimal computational overhead. These findings highlight the potential of the proposed framework to enhance communication performance in dynamic, densely populated urban environments.