Building energy management and forecasting using artificial intelligence: Advance technique

Huang, Jueru; Koroteev, Dmitry D.; Rynkovskaya, Marina

doi:10.1016/j.compeleceng.2022.107790

Cited by 11 publications

(3 citation statements)

References 20 publications

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“…Similarly, Panapakidis and Dagoumas [34] proposed diverse ANN topologies based on clustering algorithms to make their predictions. Many other techniques can be found in the literature for the same purpose [35]; some examples are deep learning [25,36], fuzzy logic [37], and tree-based [38] solutions. In [39], the authors presented a hybrid model called EA-XGB for building energy prediction and compared its performance with ARIMA and XGB models.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Prediction of Spanish Energy Pricing and Its Impact on Electric Consumption

Rodríguez

Ruiz

Criado-Ramón

et al. 2023

MAKE

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The energy supply sector faces significant challenges, such as the ongoing COVID-19 pandemic and the ongoing conflict in Ukraine, which affect the stability and efficiency of the energy system. In this study, we highlight the importance of electricity pricing and the need for accurate models to estimate electricity consumption and prices, with a focus on Spain. Using hourly data, we implemented various machine learning models, including linear regression, random forest, XGBoost, LSTM, and GRU, to forecast electricity consumption and prices. Our findings have important policy implications. Firstly, our study demonstrates the potential of using advanced analytics to enhance the accuracy of electricity price and consumption forecasts, helping policymakers anticipate changes in energy demand and supply and ensure grid stability. Secondly, we emphasize the importance of having access to high-quality data for electricity demand and price modeling. Finally, we provide insights into the strengths and weaknesses of different machine learning algorithms for electricity price and consumption modeling. Our results show that the LSTM and GRU artificial neural networks are the best models for price and consumption modeling with no significant difference.

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Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Prediction of Spanish Energy Pricing and Its Impact on Electric Consumption

Rodríguez

Ruiz

Criado-Ramón

et al. 2023

MAKE

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“…With the explosive growth of mobile data and more and more advanced data collection solutions in society, creating a digital model of public affairs can provide great reference and assistance for public management [3]. Although artificial intelligence is a new cutting-edge scientific and technological achievement in recent years, artificial intelligence has been widely used in the field of social and public management, in social life, public decisionmaking, economic management, education management, and other aspects of the more in-depth application, and constantly used in the innovation of social and public administration, and the current work and life of the public and artificial intelligence have been inextricably linked [4][5][6]. However, at present, due to the development of artificial intelligence for a short period of time, the relevant legal and regulatory mechanisms, talent training and data security management mechanisms, and other construction are still relatively lagging behind and insufficiently sound artificial intelligence in the use of China's social and public administration innovation at the same time, there are still a number of problems that need to be solved [7][8].…”

Section: Introductionmentioning

confidence: 99%

A Legal and Ethical Review of Artificial Intelligence Technology in Public Safety Management

Ning

2024

Applied Mathematics and Nonlinear Sciences

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The imperative to meticulously assess and manage the legal and ethical risks associated with artificial intelligence (AI) in public safety management is increasingly recognized. This study employs the Apriori algorithm to identify frequent itemsets in public safety risk management, further refining these findings using the FP-growth algorithm’s Gini coefficient to pinpoint optimal features representing legal-ethical risk factors. Cloud modeling techniques are also applied to examine the nuances of AI’s legal ethics. Our analysis reveals a significant growth in AI patent applications within the public safety sector, showing an increase in the relative growth rate from 1.1679 to 1.4810 over eight years, equating to an 88.66% rise. Based on highest membership values in the risk prevention and control system, risk categorization identified social ethics risk and public security threat risk with indices of 0.461, 0.721, and 0.499, respectively, classifying them into class II and III risks. This investigation into AI’s legal ethics forms a critical foundation for developing a risk regulation framework and offers strategic recommendations for legal reform, ensuring AI’s positive trajectory in public safety.

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“…The intermittent nature of renewable resources, i.e., solar radiation and wind speed, introduce uncertainty to renewable power generation. To handle the challenge of uncertainty, forecasting is a widely used technique, which can provide a forecasting value or a forecasting interval [5,6]. Lee et al [7] developed a wind power and electricity price probabilistic forecasting model, based on which an advanced offer curve was further built in the day-ahead electricity market.…”

Section: Introductionmentioning

confidence: 99%

Wind Power Bidding Based on an Ensemble Differential Evolution Algorithm with a Problem-Specific Constraint-Handling Technique

Huang,

Zhao,

et al. 2024

Energies

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The intermittent nature of wind power generation induces great challenges for power bidding in the electricity market. The deployment of battery energy storage can improve flexibility for power bidding. This paper investigates an optimal power bidding strategy for a wind–storage hybrid power plant in the day-ahead electricity market. To handle the challenges of the uncertainties of wind power generation and electricity prices, the optimal bidding problem is formulated as a risk-aware scenario-based stochastic programming, in which a number of scenarios are generated using a copula-based approach to represent the uncertainties. These scenarios consider the temporal correlation of wind power generation and electricity prices between consecutive time intervals. In the stochastic programming, a more practical but nonlinear battery operation cost function is considered, which leads to a nonlinear constrained optimization problem. To solve the nonlinear constrained optimization problem, an ensemble differential evolution (EDE) algorithm is proposed, which makes use of the merits of an ensemble of mutant operators to generate mutant vectors. Moreover, a problem-specific constraint-handling technique is developed. To validate the effectiveness of the proposed EDE algorithm, it is compared with state-of-the-art DE-based algorithms for constrained optimization problems, including a constrained composite DE (C2oDE) algorithm and a novel DE (NDE) algorithm. The experimental results demonstrate that the EDE algorithm is much more reliable and much faster in finding a better bidding strategy against benchmarking algorithms. More precisely, the average values of the success rate are 0.893, 0.667, and 0.96 for C2oDE, NDE, and EDE, respectively. Compared to C2oDE and NDE, the average value of the mean number of function evaluations to succeed with EDE is reduced by 76% and 59%, respectively.

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Building energy management and forecasting using artificial intelligence: Advance technique

Cited by 11 publications

References 20 publications

Artificial Intelligence-Based Prediction of Spanish Energy Pricing and Its Impact on Electric Consumption

Artificial Intelligence-Based Prediction of Spanish Energy Pricing and Its Impact on Electric Consumption

A Legal and Ethical Review of Artificial Intelligence Technology in Public Safety Management

Wind Power Bidding Based on an Ensemble Differential Evolution Algorithm with a Problem-Specific Constraint-Handling Technique

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