A District Energy Management Approach Based on Internet of Things and Blockchain

Olivieri, Giuseppe; Volpe, Gaetano; Mangini, Agostino Marcello; Fanti, Maria Pia

doi:10.1109/dasc/picom/cbdcom/cy55231.2022.9927910

Cited by 2 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the end of the billing period, the blockchain classifies the users in n Consumption Classes and a reward or penalty is assigned to each class. Hence, the blockchain decides all the user payments based on a virtuous classification performed by a K-means clustering algorithm proposed in [10]. In particular, the users are classified into consumption classes by a Kmeans clustering algorithm, and each class is characterized by a multiplier coefficient that increases (penalty) or decreases (reward) the user energy costs.…”

Section: Hvac Control Systemmentioning

confidence: 99%

“…A Consumption Class c j basically represents the user's consumption profile in a certain time frame h ∈ N , where N is the set of natural numbers, and a billing period is made of a certain number of time frames. As suggested in [10], the profile can be driven by the level of compliance of the user to the law and to the best practices in terms of energy saving and environment preservation.…”

Section: A K-means District User Clusteringmentioning

confidence: 99%

“…Moreover, at the end of the billing period, the blockchain classifies the users into Consumption Classes, and rewards or penalties are assigned to each class. Hence, the blockchain decides all the user payments based on a virtuous classification performed by a K-means clustering algorithm proposed in our previous paper [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A User Based HVAC System Management Through Blockchain Technology and Model Predictive Control

Olivieri,

Volpe,

Mangini

et al. 2024

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

This paper introduces an innovative approach to designing a user-based Heating, Ventilation, and Air-Conditioning (HVAC) system management connected with the District Energy Management System. By classifying the users into dynamic energy consumption classes to reward energy efficiency and penalize excessive use, users can modify their behavior to pass to a less expensive and more virtuous consumption class. To this aim, a blockchain platform determines the rewards and penalties and, by a K-means clustering algorithm, categorizes users into respective groups. Then, a Class Follower Problem is formulated and solved by a Model Predictive Control (MPC) strategy integrated with a Long Short-Term Memory network as a predictive model. If the users follow the suggestions proposed by the controller, i.e., the thermostat set-points and the time intervals in which the HVAC system must be switched off or on, the users can be located in a more virtuous consumption class. A case study conducted within an energy district in Bari (Italy) shows how the proposed architectural framework tuned thermal regulation in intelligent buildings while concurrently achieving energy optimization.Note to Practitioners-This paper addresses the challenge of efficiently managing HVAC systems in smart districts through a novel blockchain-based framework and an optimization strategy solved by an MPC approach. The objective is to incentivize users to optimize their energy consumption by introducing dynamic Consumption Classes that reward energy efficiency and penalize inefficient utilization. For practitioners, this strategy translates to a granular level of energy management that not only adapts to individual behaviors but also aligns with broader sustainability goals. Integrating the blockchain platform ensures a transparent and secure method for managing and recording energy usage. At the same time, adopting MPC with Long Short-Term Memory Networks offers accurate forecasts and adjustments to enhance system responsiveness. Although the study focuses on HVAC systems, the principles may be extended to other energy-intensive applications, providing a comprehensive tool for energy management and user engagement in smart cities. Future research could integrate renewable energy sources and explore the implications of user-driven adjustments on the overall energy distribution and efficiency.

show abstract

Section: Hvac Control Systemmentioning

confidence: 99%

Section: A K-means District User Clusteringmentioning

confidence: 99%