Artificial intelligence based smart energy community management: A reinforcement learning approach

Zhou, Suyang; Hu, Zijian; Gu, Wei Yong; Jiang, Meng; Zhang, Xiaoping

doi:10.17775/cseejpes.2018.00840

Cited by 100 publications

(74 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Various techniques, such as fuzzy logic [63], cooperative game theory [64], genetic algorithms [65], and deep recurrent neural networks [66], have been proposed. Specifically, a reinforcement learning-based strategy (Fuzzy Q-learning) was recently proposed to improve the decision-making process of P2P power trading [67]. These strategies may be combined with our privacy-enhancing method.…”

Section: Discussionmentioning

confidence: 99%

Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption

Son

Kwon

et al. 2020

Energies

View full text Add to dashboard Cite

Advanced smart grid technologies enable energy prosumers to trade surplus energy from their distributed renewable energy sources with other peer prosumers through peer-to-peer (P2P) energy trading. In many previous works, P2P energy trading was facilitated by blockchain technology through blockchain’s distributive nature and capacity to run smart contracts. However, the feature that all the data and transactions on a blockchain are visible to all blockchain nodes may significantly threaten the privacy of the parties participating in P2P energy trading. There are many previous works that have attempted to mitigate this problem. However, all these works focused on the anonymity of participants but did not protect the data and transactions. To address this issue, we propose a P2P energy trading system on a blockchain where all bids are encrypted and peer matching is performed on the encrypted bids by a functional encryption-based smart contract. The system guarantees that the information encoded in the encrypted bids is protected, but the peer matching transactions are performed by the nodes in a publicly verifiable manner through smart contracts. We verify the feasibility of the proposed system by implementing a prototype composed of smart meters, a distribution system operator (DSO) server, and private Ethereum blockchain.

show abstract

Section: Discussionmentioning

confidence: 99%

Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption

Son

Kwon

et al. 2020

Energies

View full text Add to dashboard Cite

show abstract

“…Table 5 summarizes the major machine learning algorithms used in building control stage. MPC [96], [97], [98], [99] Kalman Filter [100], [101] Generic Algorithm [102] RL: value based [103], [104], [105], [106], [107], [108] RL: actor critic [109], [110] Learning building thermal dynamics for building control RC model and regression [97], [111], [112], [113] RC model and Generic Algorithm [114] Lighting control RL: value based [115] Window control RL: value based [116] Thermal Energy Storage control Non-linear programming [117] RL: value based [118] RL: actor critic [119], [120] Hot water control RL: value based [121] RL: actor critic [122] Comfort improvement…”

Section: Machine Learning For Building Controlmentioning

confidence: 99%

“…The first is fuzzy Q-learning, which uses fuzzy rules to map the continuous state-action space to discrete state-action pairs. Yu and Dexter (2010) applied fuzzy Q-learning to control an HVAC set point [105], and Zhou et al (2019) used fuzzy Q-learning to manage the smart grid [106]. The second approach is to use value function approximation, i.e., using some approximation function to regress the mapping between state-action pairs to values.…”

Section: B) Value-basedmentioning

confidence: 99%

State-of-the-art on research and applications of machine learning in the building life cycle

Hong

Wang

Luo

et al. 2020

Energy and Buildings

232

View full text Add to dashboard Cite

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

show abstract

“…Nevertheless, the different physical characteristics and complex coupling among different energy sources have made establishing precise mathematical models of IES systems a challenge for researchers, which has correspondingly rendered a number of traditional model-based approaches unsuitable for IES management. Artificial intelligence (AI) methods have achieved more satisfactory results than traditional modeling methods in data analysis and decision optimization problems [3,4]. On the other hand, an explosion in the availability of data has been witnessed over the last decade.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Energy System Operating Scenarios Generator Based on Generative Adversarial Network

Zhou

Zhong

et al. 2019

Sustainability

Self Cite

View full text Add to dashboard Cite

The convergence of energy security and environmental protection has given birth to the development of integrated energy systems (IES). However, the different physical characteristics and complex coupling of different energy sources have deeply troubled researchers. With the rapid development of AI and big data, some attempts to apply data-driven methods to IES have been made. Data-driven technologies aim to abandon complex IES modeling, instead mining the mapping relationships between different parameters based on massive volumes of operating data. However, integrated energy system construction is still in the initial stage of development and operational data are difficult to obtain, or the operational scenarios contained in the data are not enough to support data-driven technologies. In this paper, we first propose an IES operating scenario generator, based on a Generative Adversarial Network (GAN), to produce high quality IES operational data, including energy price, load, and generator output. We estimate the quality of the generated data, in both visual and quantitative aspects. Secondly, we propose a control strategy based on the Q-learning algorithm for a renewable energy and storage system with high uncertainty. The agent can accurately map between the control strategy and the operating states. Furthermore, we use the original data set and the expanded data set to train an agent; the latter works better, confirming that the generated data complements the original data set and enriches the running scenarios.

show abstract

Artificial intelligence based smart energy community management: A reinforcement learning approach

Cited by 100 publications

References 0 publications

Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption

Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption

State-of-the-art on research and applications of machine learning in the building life cycle

An Integrated Energy System Operating Scenarios Generator Based on Generative Adversarial Network

Contact Info

Product

Resources

About