Grid reliability enhancement by peak load forecasting with a PSO hybridized ANN model

Takiyar, Swati

doi:10.1109/icrito.2015.7359274

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…These systems are modeled using techniques (e.g., support vector machines (SVMs), Markov chains, ANNs, fuzzy neural networks, and stochastic distributions). Moreover, ANNs can be further classified into supervised, unsupervised, and RL models [51,52].…”

Section: Historical Development Of Load Forecasting Modelsmentioning

confidence: 99%

A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies

Zaboli,

Kasimalla,

Park

et al. 2024

Energies

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Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model’s precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model’s predictive capabilities. Comparing the model’s predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach.

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Section: Historical Development Of Load Forecasting Modelsmentioning

confidence: 99%

A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies

Zaboli,

Kasimalla,

Park

et al. 2024

Energies

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“…There are a number of publications in the literature that attempt to predict demand, some of which account for potential changes in demand brought about by DR. Most of the studies [77], [78], [79], [80], [81], [82], [83], [84], [85], [86] focus on estimating demand for the next day or two, while others [87] look forward a week. Also, load forecasting has been done at many other aggregation levels, including for single-family homes [78], [88], [89], [90], commercial buildings [80], [83], [91] and individual appliances [92], [93] such as chillers, ice banks, and lights.…”

Section: ) Load Forecastingmentioning

confidence: 99%

Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

et al. 2023

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Demand Response (DR) has gained popularity in recent years as a practical strategy to increase the sustainability of energy systems while reducing associated costs. Despite this, Artificial Intelligence (AI) and Machine Learning (ML), have recently developed as critical technologies for demand-side management and response due to the high complexity of tasks associated with DR, as well as huge amount of data management to take decisions very near to real time implications. Selecting the best group of users to respond, learning their attitude toward consumptions and their priorities, price optimization, monitoring and control of devices, learning to engage more and more consumers in the DR schemes, and learning how to remunerate them fairly and economically are all problems that can be tackled with the help of AI techniques. This study presents an overview of AI approaches used for DR applications. Both the Artificial Intelligence and Machine Learning algorithm(s) are employed while discussing commercial efforts (from both new and existing businesses) and large-scale innovation projects that have applied AI technologies for energy DR. Different kind of DR programs implemented in different countries are also discussed. Moreover, it also discusses the application of blockchain for DR schemes in smart grid paradigm. Discussion of the strengths and weaknesses of the evaluated AI methods for various DR tasks, as well as suggestions for further study, round out the work.INDEX TERMS Artificial intelligence, blockchain, demand response, demand side management, demand response, Internet of Things (IoT), smart grids, machine learning.

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“…Most of the information at the market level concerned predicting competitive residential pricing schemes [41, 42, and 43]. However, forecasting aggregate loads may also concentrate on evaluating day-to-day peak demand, either at the building level or at the feeder or neighborhood level [44,45]. Also, residential load forecasting was conducted at different aggregation speeds [46].…”

Section: Eai Endorsed Transactions On Industrial Network and Intelligent Systemsmentioning

confidence: 99%

Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation

Kumar¹,

Saini²

2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Hydropower is one of the most promising sources of renewable energy. However, a substantial initial investment requires for the construction of large civil structures. Feasibility study, detailed project report preparation, construction planning, and timely execution of work are the important activities of a hydropower plant. Energy generation in hydropower plants are mainly depends on discharge and head. Therefore, an accurate estimation of discharge and head is important to decide the plant capacity. Erosion, cavitation, and operation & maintenance are the key challenges in hydropower energy generation. Artificial Intelligence (AI) has become popular, which can be utilized for site selection, parameters assessment, and operation & maintenance optimization. In this paper, a literature review on applications of AI in hydropower has been presented, and an attempt has also been made to identify the future potential areas of hydropower plants.

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Grid reliability enhancement by peak load forecasting with a PSO hybridized ANN model

Cited by 5 publications

References 9 publications

A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies

A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies

Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation

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