Article
Engineering, Electrical & Electronic
Partik Kumar, Abhijit R. Abhyankar
Summary: In this paper, a Modified Factorial Hidden Markov Model (MFHMM) based Non-Intrusive Load Monitoring (NILM) framework is proposed, which enables the assessment of energy consumption behavior through modeling appliance activities and segmenting operating states.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Engineering, Electrical & Electronic
Zhao Wu, Chao Wang, Huaiqing Zhang, Wenxiong Peng, Weihua Liu
Summary: This paper introduces the application of Hidden Markov Model (HMM) and its variations in Non-Intrusive Load Monitoring (NILM). By proposing a time-efficient Factorial Hidden Semi-Markov Model (TE-FHSMM), the paper achieves a reduction in time consumption while maintaining performance when dealing with datasets with different numbers of appliances. Additionally, experiments show that TE-FHSMM outperforms six state-of-the-art algorithms in terms of Accuracy and F1 score in real-world scenarios and publicly available datasets.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Energy & Fuels
Wen Fan, Qing Liu, Ali Ahmadpour, Saeed Gholami Farkoush
Summary: The Load Disaggregation (LD) problem is addressed in this paper using a model with multi-objective functions that capture appliance features from different perspectives. Experimental results show that using more features leads to more accurate outcomes, with the proposed method achieving at least 20% higher accuracy than other methods.
Article
Construction & Building Technology
Zhao Wu, Chao Wang, Wenxiong Peng, Weihua Liu, Huaiqing Zhang
Summary: This paper introduces an Adaptive Density Peak Clustering-Factorial Hidden Markov Model (ADPC-FHMM) that can automatically determine the working states of appliances with reduced dependency on prior information. Case studies show that the proposed model outperforms its counterparts on metrics such as Accuracy, F-measure, and MAE.
ENERGY AND BUILDINGS
(2021)
Article
Energy & Fuels
Xiaochao Guo, Chao Wang, Tao Wu, Ruiheng Li, Houyi Zhu, Huaiqing Zhang
Summary: Results from Non-Intrusive Load Monitoring can be used for energy decomposition and load identification, facilitating effective energy consumption management. However, existing studies often focus on fixed settings, which limits the practical application in real-world scenarios. In this study, we propose a novel appliance detection method that analyzes pattern variations of aggregated power sequences and can determine new appliances without prior information, establishing a basis for load monitoring in scenarios with dynamic changes in load topology. Experimental results show significant improvement in F1 metric compared to event-based approaches.
Article
Computer Science, Artificial Intelligence
Hajer Salem, Moamar Sayed-Mouchaweh, Moncef Tagina
Summary: Infinite Factorial Hidden Markov Model (iFHMM) is an extension of the Factorial Hidden Markov Model for Non-Intrusive Load Monitoring (NILM), inferring the number of appliances in households and adjusting its model complexity accordingly. To overcome the challenges faced by the original model, a new version, iFHMMCC, is introduced with contextual features constraints to enhance accuracy and computational efficiency.
Article
Engineering, Electrical & Electronic
Devie Paramasivam Mohan, Kalyani Sundaram
Summary: This paper proposes a decision tree-based approach for identifying device operations and categorizing load. The approach adopts a balanced data learning method to improve classifier performance and has been evaluated and validated using different datasets.
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2023)
Article
Construction & Building Technology
Dong Xia, Shusong Ba, Ali Ahmadpour
Summary: The study proposes a practical mixed method for load disaggregation of electrical devices in smart homes, utilizing the highly precise Factorial Hidden Markov Model. The Improved Prey-Predator Optimization Algorithm is applied, along with three constraints for better adjustment of the position matrix, resulting in decreased evaluation databanks and computing periods. The efficiency of the proposed technique is confirmed through the evaluation of speed and accuracy of useful data from six smart homes against other methods.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Engineering, Electrical & Electronic
Wenpeng Luan, Fan Yang, Bochao Zhao, Bo Liu
Summary: This paper proposes two HMM-based methods for disaggregating industrial loads, utilizing extra reactive power observation and state duration distribution. Experimental results demonstrate that these methods improve the disaggregation performance for industrial devices.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Energy & Fuels
Tamara Todic, Vladimir Stankovic, Lina Stankovic
Summary: With the widespread deployment of smart meters, Non-Intrusive Load Monitoring (NILM) has emerged as a promising application for informing energy management within buildings. However, existing deep learning NILM models have limitations in terms of flexibility and scalability. In this study, an active learning framework is proposed to improve transferability and reduce the cost of labeling, achieving optimal accuracy-labelling effort trade-off. The results demonstrate the potential of this approach in improving the performance of NILM models and reducing computational resources needed.
Review
Engineering, Electrical & Electronic
Pascal A. Schirmer, Iosif Mporas
Summary: The rapid development of technology in the electrical energy sector has led to increased electric power needs. This has resulted in the adoption of smart-meters and smart-grids, as well as the rise of Load Monitoring (LM) using Non-Intrusive Load Monitoring (NILM) for appliance-specific energy monitoring. The article provides a review of NILM approaches, groups previously published results, and includes a software implementation of the described NILM approaches.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Energy & Fuels
Soumyajit Ghosh, Debashis Chatterjee
Summary: Smart meter technology is crucial in the context of smart grid connected residential load system, with non-intrusive load monitoring being a well-known method to assess power consumption and operating behavior of individual loads. The issue of identifying harmonic polluting loads has arisen due to modern household appliances injecting unwanted harmonics into the system. An improved technique using input aggregated voltage-current data and an Artificial Bee Colony algorithm has been proposed for load monitoring without heavy training mechanisms, showing promising results in comparison to state-of-the-art techniques.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2021)
Article
Chemistry, Analytical
Nasrin Kianpoor, Bjarte Hoff, Trond Ostrem
Summary: This study proposes an adaptive ensemble filtering framework integrated with long short-term memory (LSTM) for identifying flexible loads. The framework, called AEFLSTM, combines filtering techniques and LSTM to filter noise and learn the long-term dependencies of flexible loads. It also searches ensemble filtering techniques to find the best method for disaggregating different loads.
Article
Engineering, Electrical & Electronic
Marco Balletti, Veronica Piccialli, Antonio M. Sudoso
Summary: This paper proposes a novel two-stage optimization-based approach for energy disaggregation, which efficiently infers the energy consumption of each appliance. The approach leverages appliance-specific constraints and prior knowledge to overcome the drawbacks of existing optimization methods.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Mathematics
Ales Jandera, Tomas Skovranek
Summary: This work proposes a Customer behaviour hidden Markov model (CBHMM) to predict customer behavior and forecast store income in e-commerce. The model consists of three sub-models and uses a transition matrix to distinguish between decision-states of order completed, order uncompleted, or no order. The Viterbi algorithm is used to evaluate the completion of orders, followed by the estimation of forecasted store income. Comparisons with a baseline prediction model show that CBHMM outperforms in terms of R-squared criterion and has a higher PG value.
Article
Construction & Building Technology
Samiran Khorat, Debashish Das, Rupali Khatun, Sk Mohammad Aziz, Prashant Anand, Ansar Khan, Mattheos Santamouris, Dev Niyogi
Summary: Cool roofs can effectively mitigate heatwave-induced excess heat and enhance thermal comfort in urban areas. Implementing cool roofs can significantly improve urban meteorology and thermal comfort, reducing energy flux and heat stress.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Qi Li, Jiayu Chen, Xiaowei Luo
Summary: This study focuses on the vertical wind conditions as a main external factor that limits the energy assessment of high-rise buildings in urban areas. Traditional tools for energy assessment of buildings use a universal vertical wind profile estimation, without taking into account the unique wind speed in each direction induced by the various shapes and configurations of buildings in cities. To address this limitation, the study developed an omnidirectional urban vertical wind speed estimation method using direction-dependent building morphologies and machine learning algorithms.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Xiaojun Luo, Lamine Mahdjoubi
Summary: This paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy allocation and transmission among multiple domestic buildings. Machine learning is used to predict energy generation and consumption patterns, and the proposed framework establishes optimal and automated energy allocation through peer-to-peer energy transactions. The approach contributes to the reduction of greenhouse gas emissions and enhances environmental sustainability.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ying Yu, Yuanwei Xiao, Jinshuai Chou, Xingyu Wang, Liu Yang
Summary: This study proposes a dual-layer optimization design method to maximize the energy sharing potential, enhance collaborative benefits, and reduce the storage capacity of building clusters. Case studies show that the proposed design significantly improves the performance of building clusters, reduces energy storage capacity, and shortens the payback period.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Felix Langner, Weimin Wang, Moritz Frahm, Veit Hagenmeyer
Summary: This paper compares two main approaches to consider uncertainties in model predictive control (MPC) for buildings: robust and stochastic MPC. The results show that compared to a deterministic MPC, the robust MPC increases the electricity cost while providing complete temperature constraint satisfaction, while the stochastic MPC slightly increases the electricity cost but fulfills the thermal comfort requirements.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Somil Yadav, Caroline Hachem-Vermette
Summary: This study proposes a mathematical model to evaluate the performance of a Double Skin Facade (DSF) system and its impact on indoor conditions. The model considers various design parameters and analyzes their effects on the system's electrical output and room temperature.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ruijun Chen, Holly Samuelson, Yukai Zou, Xianghan Zheng, Yifan Cao
Summary: This research introduces an innovative resilient design framework that optimizes building performance by considering a holistic life cycle perspective and accounting for climate projection uncertainties. The study finds that future climate scenarios significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density being major factors. By using ensemble learning and optimization algorithms, predictions for carbon emissions, cost, and indoor discomfort hours can be made, and the best resilient design scheme can be selected. Applying this framework leads to significant improvements in building life cycle performance.
ENERGY AND BUILDINGS
(2024)