Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

Applications of Computational Intelligence to Power Systems

In power system operation and control, the basic goal is to provide users with quality electricity power in an economically rational degree for power systems, and to ensure their stability and reliability. However, the increased interconnection and loading of the power system along with deregulation and environmental concerns has brought new challenges for electric power system operation, control, and automation. In the liberalised electricity market, the operation and control of a power system has become a complex process because of the complexity in modelling and uncertainties. Computational intelligence (CI) is a family of modern tools for solving complex problems that are difficult to solve using conventional techniques, as these methods are based on several requirements that may not be true all of the time. Developing solutions with these “learning-based” tools offers the following two major advantages: the development time is much shorter than when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing data/information, known as uncertainty

Forecasting Energy Consumption using Sequence to Sequence Attention models

To combat negative environmental conditions, reduce operating costs, and identify energy savings opportunities, it is essential to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely-used smart meters, have created possibilities for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting, such as FeedForward Neural Networks, are not well-suited for interpreting the time dimensionality of a signal. Consequently, this thesis applies Sequence-to-Sequence (S2S) Recurrent Neural Networks (RNNs) with attention for electrical load forecasting. The S2S and S2S attention architectures commonly used for neural machine translation are adapted for energy forecasting. An RNN enables capturing time dependencies present in the load data, while the S2S RNN model strengthens consecutive sequence prediction by combining two RNNs: encoder and decoder. Adding the attention mechanism to these S2S RNNs alleviates the burden of connecting the encoder and decoder. Presented experiments compare a regular S2S model and four S2S attention models with two baseline models, the conventional Non-S2S RNN and a Deep Neural Network (DNN). Furthermore, each RNN model was evaluated with three different RNN-cells: Vanilla RNN, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) cell. All models were trained and tested on one building-level electrical load dataset, with five-minute incremental data. Results showed that the S2S Bahdanau et al. attention model was the dominant model as it outperformed all other models for nearly all forecasting lengths

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems

The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon

"Integrating AI and AEM Electrolyzer for Green Hydrogen Production: Optimization of Solar-Powered Electrolysis in Residential Energy Management"

openThis master's thesis presents a comprehensive study on the forecasting of short-term power generation in a grid-connected hybrid solar photovoltaic (PV) system through the utilization of an artificial intelligence (AI) model. The research integrates weather data and solar PV electricity production data to develop and optimize a Long Short-Term Memory (LSTM) based AI model. The year 2021's solar PV and weather data were utilized for training and validating the model. Additionally, AEM electrolyzer was optimized to efficiently produce hydrogen using surplus electricity generated by the solar PV system . The investigation identified notable correlations between solar radiation, solar energy, UV index, and various other weather parameters with solar PV power generation. These correlations played a significant role in enhancing the accuracy of the AI model in predicting power generation. Various LSTM model structures were evaluated, and a two-layer LSTM model demonstrated superior performance, achieving an accuracy of approximately 80%. Furthermore, surplus electricity generated by the system, averaging 10 kWh during the daytime was calculated and analyzed. The economic viability of the hybrid system was also established, as the cost of electricity generated through the hybrid system was less than half of the grid energy price, meeting the regulatory standards.Optimizing the AEM electrolyzer revealed that a configuration with a few standby parallel AEM electrolyzers was optimal for utilizing excess electricity effectively. Further than that scheduling the parallel system in hourly basis for the days ahead, would help to have more conveniently benefit from this system. In conclusion, this research presents promising avenues for future studies aimed at further enhancing the efficiency and sustainability of renewable energy systems. Prospective research includes real-time integration of weather updates for AI models, advanced energy storage systems, demand-side management strategies, comparison of machine learning algorithms, optimized hydrogen production, and the evaluation of the integrated model in a microgrid setting. These future directions aim to contribute to the wider adoption of renewable energy sources and facilitate the transition towards a more sustainable energy future.This master's thesis presents a comprehensive study on the forecasting of short-term power generation in a grid-connected hybrid solar photovoltaic (PV) system through the utilization of an artificial intelligence (AI) model. The research integrates weather data and solar PV electricity production data to develop and optimize a Long Short-Term Memory (LSTM) based AI model. The year 2021's solar PV and weather data were utilized for training and validating the model. Additionally, AEM electrolyzer was optimized to efficiently produce hydrogen using surplus electricity generated by the solar PV system . The investigation identified notable correlations between solar radiation, solar energy, UV index, and various other weather parameters with solar PV power generation. These correlations played a significant role in enhancing the accuracy of the AI model in predicting power generation. Various LSTM model structures were evaluated, and a two-layer LSTM model demonstrated superior performance, achieving an accuracy of approximately 80%. Furthermore, surplus electricity generated by the system, averaging 10 kWh during the daytime was calculated and analyzed. The economic viability of the hybrid system was also established, as the cost of electricity generated through the hybrid system was less than half of the grid energy price, meeting the regulatory standards.Optimizing the AEM electrolyzer revealed that a configuration with a few standby parallel AEM electrolyzers was optimal for utilizing excess electricity effectively. Further than that scheduling the parallel system in hourly basis for the days ahead, would help to have more conveniently benefit from this system. In conclusion, this research presents promising avenues for future studies aimed at further enhancing the efficiency and sustainability of renewable energy systems. Prospective research includes real-time integration of weather updates for AI models, advanced energy storage systems, demand-side management strategies, comparison of machine learning algorithms, optimized hydrogen production, and the evaluation of the integrated model in a microgrid setting. These future directions aim to contribute to the wider adoption of renewable energy sources and facilitate the transition towards a more sustainable energy future