Energy generation forecasting: elevating performance with machine and deep learning

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This research, conducted throughout the years 2022 and 2023, examines the role of blockchain technology in optimizing Demand Response (DR) within Smart Grids (SGs). It critically assesses a range of blockchain architectures, evaluating their impact on enhancing DR’s efficiency, security, and consumer engagement. Concurrently, it addresses challenges like scalability, interoperability, and regulatory complexities inherent in merging blockchain with existing energy systems. By integrating theoretical and practical viewpoints, it reveals the potential of blockchain technology to revolutionize Demand Response (DR). Findings affirm that integrating blockchain technology into SGs effectively enhances the efficiency and security of DR, and empirical data illustrate substantial improvements in both cases. Furthermore, key challenges include scalability and interoperability, and also identifying opportunities to enhance consumer engagement and foster system transparency in the adoption of blockchain within DR and SGs. Finally, this work emphasizes the necessity for further investigation to address development hurdles and enhance the effectiveness of blockchain technology in sustainable energy management in SGs.

Section: The Emergence Of Demand Responsementioning

confidence: 99%

Integrating Blockchain in Smart Grids for Enhanced Demand Response: Challenges, Strategies, and Future Directions

Koukaras,

Afentoulis,

Gkaidatzis

et al. 2024

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“…Meteorological data play a crucial role as input parameters for EGF and ELF. In addition to historical data, future weather data were utilised as inputs in works regarding EGF [21] and ELF [22]. These data were employed for the same time horizon as the specified step ahead of the target parameter (load or generation) that was being predicted.…”

Section: Input Parameters For Load and Generation Forecastingmentioning

confidence: 99%

“…The calibre of the training data affects how well these strategies function. The availability of such historical PV power data is a necessary prerequisite for direct PV forecasting, as ML techniques require some historical data for model training [21].…”

Section: Generation Forecastingmentioning

confidence: 99%

Energy Forecasting: A Comprehensive Review of Techniques and Technologies

Mystakidis,

Koukaras,

Tsalikidis

et al. 2024

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Distribution System Operators (DSOs) and Aggregators benefit from novel energy forecasting (EF) approaches. Improved forecasting accuracy may make it easier to deal with energy imbalances between generation and consumption. It also helps operations such as Demand Response Management (DRM) in Smart Grid (SG) architectures. For utilities, companies, and consumers to manage energy resources effectively and make educated decisions about energy generation and consumption, EF is essential. For many applications, such as Energy Load Forecasting (ELF), Energy Generation Forecasting (EGF), and grid stability, accurate EF is crucial. The state of the art in EF is examined in this literature review, emphasising cutting-edge forecasting techniques and technologies and their significance for the energy industry. It gives an overview of statistical, Machine Learning (ML)-based, and Deep Learning (DL)-based methods and their ensembles that form the basis of EF. Various time-series forecasting techniques are explored, including sequence-to-sequence, recursive, and direct forecasting. Furthermore, evaluation criteria are reported, namely, relative and absolute metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R2), and Coefficient of Variation of the Root Mean Square Error (CVRMSE), as well as the Execution Time (ET), which are used to gauge prediction accuracy. Finally, an overall step-by-step standard methodology often utilised in EF problems is presented.

“…RMSE is widely used as it is measured in the same units as the variable in question. According to its mathematical definition (Equation ( 1)), RMSE applies more weight to larger errors, given that the impact of a single error on the total is proportional to its square and not its magnitude [88].…”

Section: Root Mean Square Error (Rmse)mentioning

confidence: 99%

Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models

Koukaras,

Mustapha,

Mystakidis

et al. 2024

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The building sector, known for its high energy consumption, needs to reduce its energy use due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy usage is needed. This work optimizes short-term load forecasting (STLF) in the building sector while considering several variables (energy consumption/generation, weather information, etc.) that impact energy use. It performs a comparative analysis of various machine learning (ML) models based on different data resolutions and time steps ahead (15 min, 30 min, and 1 h with 4-step-, 2-step-, and 1-step-ahead, respectively) to identify the most accurate prediction method. Performance assessment showed that models like histogram gradient-boosting regression (HGBR), light gradient-boosting machine regression (LGBMR), extra trees regression (ETR), ridge regression (RR), Bayesian ridge regression (BRR), and categorical boosting regression (CBR) outperformed others, each for a specific resolution. Model performance was reported using R2, root mean square error (RMSE), coefficient of variation of RMSE (CVRMSE), normalized RMSE (NRMSE), mean absolute error (MAE), and execution time. The best overall model performance indicated that the resampled 1 h 1-step-ahead prediction was more accurate than the 15 min 4-step-ahead and the 30 min 2-step-ahead predictions. Findings reveal that data preparation is vital for the accuracy of prediction models and should be model-adjusted.