A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithms

Yan, Zhongzhen; Zhu, Xinyuan; Wang, Xianglong; Ye, Zhiwei; Guo, Feng; Xie, Lei; Zhang, Guiju

doi:10.1177/01445987221112250

Cited by 13 publications

(4 citation statements)

References 53 publications

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“…The backpropagation is an optimization algorithm to further adjust the loss function and minimize the prediction error [60]. To improve its performance, regularization, dropout, batch normalization, activation functions and advanced optimization algorithms can be used [61].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Bâra,

Oprea

2024

Int J Comput Intell Syst

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The energy transition to a cleaner environment has been a concern for many researchers and policy makers, as well as communities and non-governmental organizations. The effects of climate change are evident, temperatures everywhere in the world are getting higher and violent weather phenomena are more frequent, requiring clear and firm pro-environmental measures. Thus, we will discuss the energy transition and the support provided by artificial intelligence (AI) applications to achieve a cleaner and healthier environment. The focus will be on applications driving the energy transition, the significant role of AI, and collective efforts to improve societal interactions and living standards. The price of electricity is included in almost all goods and services and should be affordable for the sustainable development of economies. Therefore, it is important to model, anticipate and understand the trend of electricity markets. The electricity price includes an imbalance component which is the difference between notifications and real-time operation. Ideally it is zero, but in real operation such differences are normal due to load variation, lack of renewable energy sources (RES) accurate prediction, unplanted outages, etc. Therefore, additional energy has to be produced or some generating units are required to reduce generation to balance the power system. Usually, this activity is performed on the balancing market (BM) by the transmission system operator (TSO) that gathers offers from generators to gradually reduce or increase the output. Therefore, the prediction of the imbalance volume along with the prices for deficit and surplus is of paramount importance for producers’ decision makers to create offers on the BM. The main goal is to predict the imbalance volume and minimize the costs that such imbalance may cause. In this chapter, we propose a method to predict the imbalance volume based on the classification of the imbalance sign that is inserted into the dataset for predicting the imbalance volume. The imbalance sign is predicted using several classifiers and the output of the classification is added to the input dataset. The rest of the exogenous variables are shifted to the values from previous day d − 1. Therefore, the input variables are either predicted (like the imbalance sign) or are known from d − 1. Several metrics, such as mean average percentage error (MAPE), determination coefficient R2 and mean average error (MAE) are calculated to assess the proposed method of combining classification machine learning (ML) algorithms and recurrent neural networks (RNN) that memorize variations, namely long short-term memory (LSTM) model.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Bâra,

Oprea

2024

Int J Comput Intell Syst

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“…The modelling model developed by Yan Zhang [175] using the Light Gradient Boosting Machine combined with the Shapley Additive exPlanation algorithm can predict the energy use and greenhouse gas emissions of residential buildings and identify the most influential variables. Yan's [108] multi-layer perceptron neural network can effectively predict the multi-energy load of buildings and found that the optimization algorithm impacts the prediction effect. Muhammad Faiq's team [182] used long short-term memory (LSTM) models and weather data to predict the energy consumption of institutional buildings, and the results were better than support vector regression (SVR) and Gaussian process regression (GPR).…”

Section: Algorithms and Deep Learning For Energy Efficiencymentioning

confidence: 99%

Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy

Tahmasebinia,

Lin,

et al. 2023

Applied Sciences

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Buildings consume a significant amount of energy throughout their lifecycle; Thus, sustainable energy management is crucial for all buildings, and controlling energy consumption has become increasingly important for achieving sustainable construction. Digital twin (DT) technology, which lies at the core of Industry 4.0, has gained widespread adoption in various fields, including building energy analysis. With the ability to monitor, optimize, and predict building energy consumption in real time. DT technology has enabled sustainable building energy management and cost reduction. This paper provides a comprehensive review of the development and application of DT technology in building energy. Specifically, it discusses the background of building information modeling (BIM) and DT technology and their application in energy optimization in buildings. Additionally, this article reviews the application of DT technology in building energy management, indoor environmental monitoring, and building energy efficiency evaluation. It also examines the benefits and challenges of implementing DT technology in building energy analysis and highlights recent case studies. Furthermore, this review emphasizes emerging trends and opportunities for future research, including integrating machine learning techniques with DT technology. The use of DT technology in the energy sector is gaining momentum as efforts to optimize energy efficiency and reduce carbon emissions continue. The advancement of building energy analysis and machine learning technologies is expected to enhance prediction accuracy, optimize energy efficiency, and improve management processes. These advancements have become the focal point of current literature and have the potential to facilitate the transition to clean energy, ultimately achieving sustainable development goals.

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“…The energy consumed can be written in the following forms: Companies face the major challenge of estimating the time and cost to produce a given product (Yan et al, 2023). This task is particularly important in the case of CNC machining, where it is essential to determine the time and cost of each operation to select the most efficient machining sequence, taking into account both the cost and the energy required to perform it ( C Energy ).…”

Section: Materials and Measurementsmentioning

confidence: 99%

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

Bousnina,

Hamza,

Yahia

2023

Energy Exploration & Exploitation

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This research aims to predict the cost and energy consumption associated with pocket and groove machining using the hybrid particle swarm optimization-artificial neurons network (PSO-ANN) algorithm and the response surface method (RSM). A parametric study was conducted to determine the best predictions by adjusting the swarm population size (pop) and the number of neurons (n) in the hidden layer. The results showed that machining strategies and sequences can have a significant impact on energy consumption, reaching a difference of 99.25% between the minimum and maximum values. The cost ( Ctot) and energy consumption ( Etot) values with the PSO-ANN algorithm increased significantly by 99.99% and 92.41%, respectively, compared to the RSM model. The minimum mean square error values for Etot and Ctot with the PSO-ANN models are 3.0499 × 10−5 and 4.6296 × 10−10, respectively. This study highlights the potential of the hybrid PSO-ANN algorithm for multi-criteria prediction and highlights the potential for improved machining of 2017A alloy.

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A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithms

Cited by 13 publications

References 53 publications

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

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