Browsing by Author "Bennani, Saad Dosse"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
- A comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trendsPublication . El-Amarty, Naima; Marzouq, Manal; El Fadili, Hakim; Bennani, Saad Dosse; Ruano, AntonioSolar irradiation data are imperatively required for any solar energy-based project. The non-accessibility and uncertainty of these data can greatly affect the implementation, management, and performance of photovoltaic or thermal systems. Developing solar irradiation estimation and forecasting approaches is an effective way to overcome these issues. Practically, prediction approaches can help anticipate events by ensuring good operation of the power network and maintaining a precise balance between the demand and supply of the power at every moment. In the literature, various estimation and forecasting methods have been developed. Artificial Neural Network (ANN) models are the most commonly used methods in solar irradiation prediction. This paper aims to firstly review, analyze, and provide an overview of different aspects required to develop an ANN model for solar irradiation prediction, such as data types, data horizon, data preprocessing, forecasting horizon, feature selection, and model type. Secondly, a highly detailed state of the art of ANN-based approaches including deep learning and hybrid ANN models for solar irradiation estimation and forecasting is presented. Finally, the factors influencing prediction model performances are discussed in order to propose recommendations, trends, and outlooks for future research in this field.
- Energy disaggregation using multi-objective genetic algorithm designed neural networksPublication . Habou Laouali, Inoussa; Gomes, Isaías; Ruano, Maria; Bennani, Saad Dosse; Fadili, Hakim El; Ruano, AntonioEnergy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices.
- A new evolutionary forest model via incremental tree selection for short-term global solar irradiance forecasting under six various climatic zonesPublication . El-Amarty, Naima; Marzouq, Manal; El Fadili, Hakim; Bennani, Saad Dosse; Ruano, Antonio; Rabehi, AbdelazizThe increasing integration of solar sources into the energy mix presents significant challenges, particularly in short-term energy management. Accurate solar irradiance forecasts can greatly assist solar power plant operators and energy network managers in making informed decisions about energy production and consumption. This paper aims to develop a new accurate forecasting model for short-term global solar irradiance based on an innovative evolutionary forest approach. Our model, baptized EFITS, performs incremental tree selection through appropriate evolutionary operators maintaining a good tradeoff between accuracy and diversity, generating progressively near-optimal decision trees to construct the final evolutionary forest forecaster. This new evolution process also automatically selects near-optimal input parameters, enhancing the overall model accuracy and generalization ability. Six climatically diverse locations in Morocco and three types of inputs (endogenous, exogenous, and hybrid) are used to assess the performance of the proposed. The results demonstrate that our proposed model exhibits excellent performance across all studied sites and horizons. Among all input types, hybrid inputs delivered the best forecasting accuracy across all studied sites and horizons. Notably, the continental climate site (Bni Mellal) achieved the highest accuracy, with nRMSE ranging from 4.94% to 7.54% and nMBE from 0.71% to -0.46% for 1 to 6 h forecasts. Conversely, Ifrane city, characterized by a humid temperate climate, showed the lowest accuracy, with nRMSE ranging from 10.34% to 18.94% and nMBE from 1.21% to -1.54%. Finally, a detailed comparison with benchmarking models (random forest, bagging, gradient boosting, single decision tree, bidirectional long short-term memory network, and scaled persistence models), revealed that our model consistently outperforms them across all tested scenarios, locations, and forecasting horizons.
- Non-intrusive load monitoring of household devices using a hybrid deep learning model through convex hull-based data selectionPublication . Habou Laouali, Inoussa; Ruano, Antonio; Ruano, Maria da Graça; Bennani, Saad Dosse; Fadili, Hakim ElThe availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows users to disaggregate the usage of each device in the house using the total aggregated power signals collected from a smart meter that is typically installed in the household. It enables the monitoring of domestic appliance use without the need to install individual sensors for each device, thus minimizing electrical system complexities and associated costs. This paper proposes an NILM framework based on low frequency power data using a convex hull data selection approach and hybrid deep learning architecture. It employs a sliding window of aggregated active and reactive powers sampled at 1 Hz. A randomized approximation convex hull data selection approach performs the selection of the most informative vertices of the real convex hull. The hybrid deep learning architecture is composed of two models: a classification model based on a convolutional neural network trained with a regression model based on a bidirectional long-term memory neural network. The results obtained on the test dataset demonstrate the effectiveness of the proposed approach, achieving F1 values ranging from 0.95 to 0.99 for the four devices considered and estimation accuracy values between 0.88 and 0.98. These results compare favorably with the performance of existing approaches.