Deep learning for time series forecasting: The electric load case

Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting, which, due to its non-linear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting, by contrasting deep learning architectures on short-term forecast (one-day-ahead prediction). Specifically, the focus is on feedforward and recurrent neural networks, sequence-to-sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one

Gestion flexible des ressources dans les réseaux de nouvelle génération avec SDN

Abstract : 5G and beyond-5G/6G are expected to shape the future economic growth of multiple vertical industries by providing the network infrastructure required to enable innovation and new business models. They have the potential to offer a wide spectrum of services, namely higher data rates, ultra-low latency, and high reliability. To achieve their promises, 5G and beyond-5G/6G rely on software-defined networking (SDN), edge computing, and radio access network (RAN) slicing technologies. In this thesis, we aim to use SDN as a key enabler to enhance resource management in next-generation networks. SDN allows programmable management of edge computing resources and dynamic orchestration of RAN slicing. However, achieving efficient performance based on SDN capabilities is a challenging task due to the permanent fluctuations of traffic in next-generation networks and the diversified quality of service requirements of emerging applications. Toward our objective, we address the load balancing problem in distributed SDN architectures, and we optimize the RAN slicing of communication and computation resources in the edge of the network. In the first part of this thesis, we present a proactive approach to balance the load in a distributed SDN control plane using the data plane component migration mechanism. First, we propose prediction models that forecast the load of SDN controllers in the long term. By using these models, we can preemptively detect whether the load will be unbalanced in the control plane and, thus, schedule migration operations in advance. Second, we improve the migration operation performance by optimizing the tradeoff between a load balancing factor and the cost of migration operations. This proactive load balancing approach not only avoids SDN controllers from being overloaded, but also allows a judicious selection of which data plane component should be migrated and where the migration should happen. In the second part of this thesis, we propose two RAN slicing schemes that efficiently allocate the communication and the computation resources in the edge of the network. The first RAN slicing scheme performs the allocation of radio resource blocks (RBs) to end-users in two time-scales, namely in a large time-scale and in a small time-scale. In the large time-scale, an SDN controller allocates to each base station a number of RBs from a shared radio RBs pool, according to its requirements in terms of delay and data rate. In the short time-scale, each base station assigns its available resources to its end-users and requests, if needed, additional resources from adjacent base stations. The second RAN slicing scheme jointly allocates the RBs and computation resources available in edge computing servers based on an open RAN architecture. We develop, for the proposed RAN slicing schemes, reinforcement learning and deep reinforcement learning algorithms to dynamically allocate RAN resources.La 5G et au-delà de la 5G/6G sont censées dessiner la future croissance économique de multiples industries verticales en fournissant l'infrastructure réseau nécessaire pour permettre l'innovation et la création de nouveaux modèles économiques. Elles permettent d'offrir un large spectre de services, à savoir des débits de données plus élevés, une latence ultra-faible et une fiabilité élevée. Pour tenir leurs promesses, la 5G et au-delà de la-5G/6G s'appuient sur le réseau défini par logiciel (SDN), l’informatique en périphérie et le découpage du réseau d'accès (RAN). Dans cette thèse, nous visons à utiliser le SDN en tant qu'outil clé pour améliorer la gestion des ressources dans les réseaux de nouvelle génération. Le SDN permet une gestion programmable des ressources informatiques en périphérie et une orchestration dynamique de découpage du RAN. Cependant, atteindre une performance efficace en se basant sur le SDN est une tâche difficile due aux fluctuations permanentes du trafic dans les réseaux de nouvelle génération et aux exigences de qualité de service diversifiées des applications émergentes. Pour atteindre notre objectif, nous abordons le problème de l'équilibrage de charge dans les architectures SDN distribuées, et nous optimisons le découpage du RAN des ressources de communication et de calcul à la périphérie du réseau. Dans la première partie de cette thèse, nous présentons une approche proactive pour équilibrer la charge dans un plan de contrôle SDN distribué en utilisant le mécanisme de migration des composants du plan de données. Tout d'abord, nous proposons des modèles pour prédire la charge des contrôleurs SDN à long terme. En utilisant ces modèles, nous pouvons détecter de manière préemptive si la charge sera déséquilibrée dans le plan de contrôle et, ainsi, programmer des opérations de migration à l'avance. Ensuite, nous améliorons les performances des opérations de migration en optimisant le compromis entre un facteur d'équilibrage de charge et le coût des opérations de migration. Cette approche proactive d'équilibrage de charge permet non seulement d'éviter la surcharge des contrôleurs SDN, mais aussi de choisir judicieusement le composant du plan de données à migrer et l'endroit où la migration devrait avoir lieu. Dans la deuxième partie de cette thèse, nous proposons deux mécanismes de découpage du RAN qui allouent efficacement les ressources de communication et de calcul à la périphérie des réseaux. Le premier mécanisme de découpage du RAN effectue l'allocation des blocs de ressources radio (RBs) aux utilisateurs finaux en deux échelles de temps, à savoir dans une échelle de temps large et dans une échelle de temps courte. Dans l’échelle de temps large, un contrôleur SDN attribue à chaque station de base un certain nombre de RB à partir d'un pool de RB radio partagé, en fonction de ses besoins en termes de délai et de débit. Dans l’échelle de temps courte, chaque station de base attribue ses ressources disponibles à ses utilisateurs finaux et demande, si nécessaire, des ressources supplémentaires aux stations de base adjacentes. Le deuxième mécanisme de découpage du RAN alloue conjointement les RB et les ressources de calcul disponibles dans les serveurs de l’informatique en périphérie en se basant sur une architecture RAN ouverte. Nous développons, pour les mécanismes de découpage du RAN proposés, des algorithmes d'apprentissage par renforcement et d'apprentissage par renforcement profond pour allouer dynamiquement les ressources du RAN

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape

Operational solar forecasting for the real-time market

Despite the significant progress made in solar forecasting over the last decade, most of the proposed models cannot be readily used by independent system operators (ISOs). This article proposes an operational solar forecasting algorithm that is closely aligned with the real-time market (RTM) forecasting requirements of the California ISO (CAISO). The algorithm first uses the North American Mesoscale (NAM) forecast system to generate hourly forecasts for a 5-h period that are issued 12 h before the actual operating hour, satisfying the lead-time requirement. Subsequently, the world's fastest similarity search algorithm is adopted to downscale the hourly forecasts generated by NAM to a 15-min resolution, satisfying the forecast-resolution requirement. The 5-h-ahead forecasts are repeated every hour, following the actual rolling update rate of CAISO. Both deterministic and probabilistic forecasts generated using the proposed algorithm are empirically evaluated over a period of 2 years at 7 locations in 5 climate zones

Machine Learning Applications for Load Predictions in Electrical Energy Network

In this work collected operational data of typical urban and rural energy network are analysed for predictions of energy consumption, as well as for selected region of Nordpool electricity markets. The regression techniques are systematically investigated for electrical energy prediction and correlating other impacting parameters. The k-Nearest Neighbour (kNN), Random Forest (RF) and Linear Regression (LR) are analysed and evaluated both by using continuous and vertical time approach. It is observed that for 30 minutes predictions the RF Regression has the best results, shown by a mean absolute percentage error (MAPE) in the range of 1-2 %. kNN show best results for the day-ahead forecasting with MAPE of 2.61 %. The presented vertical time approach outperforms the continuous time approach. To enhance pre-processing stage, refined techniques from the domain of statistics and time series are adopted in the modelling. Reducing the dimensionality through principal components analysis improves the predictive performance of Recurrent Neural Networks (RNN). In the case of Gated Recurrent Units (GRU) networks, the results for all the seasons are improved through principal components analysis (PCA). This work also considers abnormal operation due to various instances (e.g. random effect, intrusion, abnormal operation of smart devices, cyber-threats, etc.). In the results of kNN, iforest and Local Outlier Factor (LOF) on urban area data and from rural region data, it is observed that the anomaly detection for the scenarios are different. For the rural region, most of the anomalies are observed in the latter timeline of the data concentrated in the last year of the collected data. For the urban area data, the anomalies are spread out over the entire timeline. The frequency of detected anomalies where considerably higher for the rural area load demand than for the urban area load demand. Observing from considered case scenarios, the incidents of detected anomalies are more data driven, than exceptions in the algorithms. It is observed that from the domain knowledge of smart energy systems the LOF is able to detect observations that could not have detected by visual inspection alone, in contrast to kNN and iforest. Whereas kNN and iforest excludes an upper and lower bound, the LOF is density based and separates out anomalies amidst in the data. The capability that LOF has to identify anomalies amidst the data together with the deep domain knowledge is an advantage, when detecting anomalies in smart meter data. This work has shown that the instance based models can compete with models of higher complexity, yet some methods in preprocessing (such as circular coding) does not function for an instance based learner such as k-Nearest Neighbor, and hence kNN can not option for this kind of complexity even in the feature engineering of the model. It will be interesting for the future work of electrical load forecasting to develop solution that combines a high complexity in the feature engineering and have the explainability of instance based models.publishedVersio

AI-driven approaches for optimizing the energy efficiency of integrated energy system

To decarbonize the global energy system and replace the unidirectional architecture of existing grid networks, integrated and electrified energy systems are becoming more demanding. Energy integration is critical for renewable energy sources like wind, solar, and hydropower. However, there are still specific challenges to overcome, such as their high reliance on the weather and the complexity of their integrated operation. As a result, this research goes through the study of a new approach to energy service that has arisen in the shape of data-driven AI technologies, which hold tremendous promise for system improvement while maximizing energy efficiency and reducing carbon emissions. This research aims to evaluate the use of data-driven AI techniques in electrical integrated energy systems, focusing on energy integration, operation, and planning of multiple energy supplies and demand. Based on the formation point, the main research question is: "To what extent do AI algorithms contribute to attaining greater efficiency of integrated grid systems?". It also included a discussion on four key research areas of AI application: Energy and load prediction, fault prediction, AI-based technologies IoT used for smart monitoring grid system optimization such as energy storage, demand response, grid flexibility, and Business value creation. The study adopted a two-way approach that includes empirical research on energy industry expert interviews and a Likert scale survey among energy sector representatives from Finland, Norway, and Nepal. On the other hand, the theoretical part was from current energy industry optimization models and a review of publications linked to a given research issue. The research's key findings were AI's significant potential in electrically integrated energy systems, which concluded AI's implication as a better understanding of energy consumption patterns, highly effective and precise energy load and fault prediction, automated energy management, enhanced energy storage system, more excellent business value, a smart control center, smooth monitoring, tracking, and communication of energy networks. In addition, further research directions are prospects towards its technical characteristics on energy conversion

Towards intelligent operation of future power system: bayesian deep learning based uncertainty modelling technique

The increasing penetration level of renewable energy resources (RES) in the power system brings fundamental changes of the system operating paradigms. In the future, the intermittent nature of RES and the corresponding smart grid technologies will lead to a much more volatile power system with higher level uncertainties. At the same time, as a result of the larger scale installation of advanced sensor devices in power system, power system engineers for the first time have the opportunity to gain insights from the influx of massive data sets in order to improve the system performance in various aspects. To this end, it is imperative to explore big data methodologies with the aim of exploring the uncertainty space within such complex data sets and thus supporting real-time decision-making in future power system. In this thesis, Bayesian Deep learning is investigated with the aim of exploring data-driven methodologies to deal with uncertainties which is in the following three aspects. (1) The first part of this thesis proposes a novel probabilistic day-ahead net load forecasting method to capture both epistemic uncertainty and aleatoric uncertainty using Bayesian deep long short-term memory network. The proposed methodological framework employs clustering in sub-profiles and considers residential rooftop PV outputs as input features to enhance the performance of aggregated net load forecasting. Numerical experiments have been carried out based on fine-grained smart meter data from the Australian grid with separately recorded measurements of rooftop PV generation and loads. The results demonstrate the superior performance of the proposed scheme compared with a series of state-of-the-art methods and indicate the importance and effectiveness of sub-profile clustering and high PV visibility. (2) The second part of this thesis studies a novel Conditional Bayesian Deep Auto-Encoder (CBDAC) based security assessment framework to compute a confidence metric of the prediction. This informs not only the operator to judge whether the prediction can be trusted, but it also allows for judging whether the model needs updating. A case study based on IEEE 68-bus system demonstrates that CBDAC outperforms the state-of-the-art machine learning-based DSA methods and the models that need updating under different topologies can be effectively identified. Furthermore, the case study verifies that effective updating of the models is possible even with very limited data. (3) The last part of this thesis proposes a novel Bayesian Deep Reinforcement Learning-based resilient control approach for multi-energy micro-grid. In particular, the proposed approach replaces deterministic network in traditional Reinforcement Learning with Bayesian probabilistic network in order to obtain an approximation of the value function distribution, which effectively solves Q-value overestimation issue. The proposed model is able to provide both energy management during normal operating conditions and resilient control during extreme events in a multi-energy micro-grid system. Comparing with naive DDPG method and optimisation method, the effectiveness and importance of employing Bayesian Reinforcement Learning approach is investigated and illustrated across different operating scenarios. Case studies have shown that by using the Monte Carlo posterior mean of the Bayesian value function distribution instead of a deterministic estimation, the proposed BDDPG method achieves a near-optimum policy in a more stable process, which verifies the robustness and the practicability of the proposed approach.Open Acces