Incentive-based demand response for smart grid with reinforcement learning and deep neural network

Lu, Renzhi; Hong, Seung Ho

doi:10.1016/j.apenergy.2018.12.061

Cited by 336 publications

(135 citation statements)

References 42 publications

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“…proposed an energy consumption prediction system based on deep learning with edge computing [62]. Lu and Hong (2019) proposed a demand response framework using reinforcement learning to calculate the rate of an incentive to the customers for balancing energy fluctuations [63]. All the above contribute to the prediction of the daily energy consumption of each household, which plays a crucial role in the awareness of the citizens about the achieved energy savings keeping in consideration that if the feedback is to be provided to the citizens, the above process must happen in real-time.…”

Section: Sensor-based Energy Savingsmentioning

confidence: 99%

From Intelligent Energy Management to Value Economy through a Digital Energy Currency: Bahrain City Case Study

Marinakis

Doukas

Koasidis

et al. 2020

Sensors

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The transition of the energy system into a more efficient state requires innovative ideas to finance new schemes and engage people into adjusting their behavioural patterns concerning consumption. Effective energy management combined with Information and Communication Technologies (ICTs) open new opportunities for local and regional authorities, but also for energy suppliers, utilities and other obligated parties, or even energy cooperatives, to implement mechanisms that allow people to become more efficient either by producing and trading energy or by reducing their energy consumption. In this paper, a novel framework is proposed connecting energy savings with a digital energy currency. This framework builds reward schemes where the energy end-users could benefit financially from saving energy, by receiving coins according to their real consumption compared to the predicted consumption if no actions were to take place. A pilot appraisal of such a scheme is presented for the case of Bahrain, so as to simulate the behaviour of the proposed framework in order for it to become a viable choice for intelligent energy management in future action plans.

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Section: Sensor-based Energy Savingsmentioning

confidence: 99%

From Intelligent Energy Management to Value Economy through a Digital Energy Currency: Bahrain City Case Study

Marinakis

Doukas

Koasidis

et al. 2020

Sensors

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“…ANN is used to assist service providers to discover the future rates to purchase energy from its customers to balance energy fluctuations in the power system. To cope with the future uncertainties in a power system due to its inherent nature, a supervised learning in deep neural networks (DNNs) is used to predict the real time unknown load demand and wholesale market prices instead of day-ahead to incentivize the active subscribed consumer [39].…”

Section: Background Literaturementioning

confidence: 99%

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

et al. 2020

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In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company.

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“…Typically, these seasonal and weekly effects should be taken into account when selecting features and modeling. For instance, the season and day of the week are regarded as categorical variables; hence, they are usually transformed into integers [4] or one-hot encoded vectors [23]. However, in our work, we assume that SOM automatically filters out these features.…”

Section: Exploratory Data Analysis (Eda) For Load Datamentioning

confidence: 99%

“…In addition, distributed energy resources such as photovoltaic (PV) generators, energy storage systems (ESSs), and electric vehicle (EV) charging stations are increasingly being integrated into buildings. Moreover, demand response (DR) has become an efficient way to balance the power system to improve its stability, such that the economic efficiency of the entire power system increases [4]. With the background of the deregulated electricity market, building owners may be able to sell their surplus electricity by adequately scheduling or controlling their energy resources and electricity loads.…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method

Lee

Kim

2019

Applied Sciences

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Electric load forecasting for buildings is important as it assists building managers or system operators to plan energy usage and strategize accordingly. Recent increases in the adoption of advanced metering infrastructure (AMI) have made building electrical consumption data available, and this has increased the feasibility of data-driven load forecasting. Self-organizing map (SOM) has been successfully utilized to cluster a dataset into subsets containing similar data points. These subsets are then used to train the forecasting models to improve forecasting accuracy. However, some buildings may have insufficient data since newly installed monitoring devices such as AMI have no choice but to collect a limited amount of data. Using a clustering technique on small datasets could lead to overfitting when using forecasting models following an SOM network to be trained with clusters. This results in a relatively high generalization error. In this study, we propose to address this problem by employing the stacking ensemble learning method (SELM) that is well-known for its generalization ability. An experimental study was conducted using the electricity consumption data of an actual institutional building and meteorological data. Our proposed model outperformed other baseline models, which means it successfully mitigates the effect of overfitting.

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Incentive-based demand response for smart grid with reinforcement learning and deep neural network

Cited by 336 publications

References 42 publications

From Intelligent Energy Management to Value Economy through a Digital Energy Currency: Bahrain City Case Study

From Intelligent Energy Management to Value Economy through a Digital Energy Currency: Bahrain City Case Study

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method

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