Loubna Benabbou

Ismail Belhaj

Abdelali Djdiaa

Hicham Bouzekri

Abdelaziz Berrado

2024-01-01

IFAC-PapersOnLine (publié)

Generative Adversarial Neural Networks for Realistic Stock Market Simulations

Badre Labiad

Abdelaziz Berrado

—Stock market simulations are widely used to create synthetic environments for testing trading strategies before deploying them to real-ti… (voir plus)me markets. However, the weak realism often found in these simulations presents a significant challenge. Improving the quality of stock market simulations could be facilitated by the availability of rich and granular real Limit Order Books (LOB) data. Unfortunately, access to LOB data is typically very limited. To address this issue, a framework based on Generative Adversarial Networks (GAN) is proposed to generate synthetic realistic LOB data. This generated data can then be utilized for simulating downstream decision-making tasks, such as testing trading strategies, conducting stress tests, and performing prediction tasks. To effectively tackle challenges related to the temporal and local dependencies inherent in LOB structures and to generate highly realistic data, the framework relies on a specific data representation and preprocessing scheme, transformers, and conditional Wasserstein GAN with gradient penalty. The framework is trained using the FI-2010 benchmark dataset and an ablation study is conducted to demonstrate the importance of each component of the proposed framework. Moreover, qualitative and quantitative metrics are proposed to assess the quality of the generated data. Experimental results indicate that the framework outperforms existing benchmarks in simulating realistic market conditions, thus demonstrating its effectiveness in generating synthetic LOB data for diverse downstream tasks.

2024-01-01

International Journal of Advanced Computer Science and Applications (publié)

Deep Learning Model for Multi-Step Ahead Prediction of Solar Irradiance: Case of Study of Morocco

Saad Benbrahim

Ismail Belhaj

Abdelali Djdiaa

Hicham Bouzekri

Abdelaziz Berrado

Accurate solar irradiance forecasting is crucial for managing energy generation and consumption in the rapidly evolving landscape of renewab… (voir plus)le energy. It enables renewable energy operators to make informed decisions and maximize their output. This study employs deep learning-based forecasting models to predict the Global Horizontal Irradiance (GHI) of the R&D platform situated in Ouarzazate, Morocco. A sensitivity analysis was conducted on multiple scenarios for a one day-ahead horizon. Moreover, a forecasting technique that encompasses numerous horizons, ranging from one day to three days in advance, was evaluated. The study's findings suggest that the encoder-decoder model we proposed exhibited superior performance compared to the other models tested and produced dependable predictions.

2023-11-22

2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA) (publié)

Towards an Effective Electrical Market Design: Identifying and Defining Key Criteria for Decision-Making

Souhaila Chiguer

Ismail Belhaj

Abdelali Djdiaa

Hicham Bouzekri

Abdelaziz Berrado

In our changing energy landscape, electricity is taking a major role in achieving decarbonization goals. Electricity can be a clean and effi… (voir plus)cient source of energy, and it is well-suited to help countries meet their climate goals. However, the electrical market is complex and constantly evolving, and it is important to carefully choose the design elements of the market to ensure that it is meeting its objectives. In this context, evaluating an electrical market's effectiveness requires a multifaceted approach that takes into account a range of elements, from environmental impact to economic viability. This paper provides an overview of several evaluation methods for different objectives to finally select the key criteria to consider in assisting decision-makers, regulators, and stakeholders in developing an electricity market that is not only effective but also reliable and sustainable.

2023-11-22

2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA) (publié)

Predicting Solar PV Output Based on Hybrid Deep Learning and Physical
Models: Case Study of Morocco

Samira Abousaid

Ismail Belhaj

Abdelaziz Berrado

Hicham Bouzekri

2023-10-13

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) (publié)

Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context

Ghait Boukachab

Dan Assouline

Stefano Massaroli

Tianle Yuan

Solar power harbors immense potential in mitigating climate change by substantially reducing CO…

What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?

Ghait Boukachab

Dan Assouline

Stefano Massaroli

Tianle Yuan

The global integration of solar power into the electrical grid could have a crucial impact on climate change mitigation, yet poses a challen… (voir plus)ge due to solar irradiance variability. We present a deep learning architecture which uses spatio-temporal context from satellite data for highly accurate day-ahead time-series forecasting, in particular Global Horizontal Irradiance (GHI). We provide a multi-quantile variant which outputs a prediction interval for each time-step, serving as a measure of forecasting uncertainty. In addition, we suggest a testing scheme that separates easy and difficult scenarios, which appears useful to evaluate model performance in varying cloud conditions. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective use of solar power and the resulting reduction of CO

2023-07-28

ICML.cc/2023/Workshop/SynS_and_ML (publié)

What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?

Ghait Boukachab

Dan Assouline

Stefano Massaroli

Tianle Yuan

2023-07-28

ICML.cc/2023/Workshop/SynS_and_ML (publié)

MAgNet: Mesh Agnostic Neural PDE Solver

Dan Assouline

The computational complexity of classical numerical methods for solving Partial Differential Equations (PDE) scales significantly as the res… (voir plus)olution increases. As an important example, climate predictions require fine spatio-temporal resolutions to resolve all turbulent scales in the fluid simulations. This makes the task of accurately resolving these scales computationally out of reach even with modern supercomputers. As a result, current numerical modelers solve PDEs on grids that are too coarse (3km to 200km on each side), which hinders the accuracy and usefulness of the predictions. In this paper, we leverage the recent advances in Implicit Neural Representations (INR) to design a novel architecture that predicts the spatially continuous solution of a PDE given a spatial position query. By augmenting coordinate-based architectures with Graph Neural Networks (GNN), we enable zero-shot generalization to new non-uniform meshes and long-term predictions up to 250 frames ahead that are physically consistent. Our Mesh Agnostic Neural PDE Solver (MAgNet) is able to make accurate predictions across a variety of PDE simulation datasets and compares favorably with existing baselines. Moreover, MAgNet generalizes well to different meshes and resolutions up to four times those trained on.