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Forecast electricity demand in commercial building with machine learning models to enable demand response programs

能源与人工智能(英文) 2022年第1期7卷 18-30页

作者： Fabiano Pallonetto Changhong Jin eleni mangina School of Computer Science University College DublinIreland School of Business Maynooth UniversityIreland

Electricity load forecasting is an important part of power system *** forecasting electricity load have great impact on a number of departments in power *** to electricity load simulation(white-box model),electricity load forecasting(black-box model)does not require expertise in building *** development cycle of the electricity load forecasting model is much shorter than the design cycle of the electricity load *** developments in machine learning have lead to the creation of models with strong fitting and accuracy to deal with nonlinear *** on the real load dataset,this paper evaluates and compares the two mainstream short-term load forecasting *** the experiment,this paper first enumerates the common methods of short-term load forecasting and explains the principles of Long Short-term Memory Networks(LSTMs)and Support Vector Machines(SVM)used in this ***,based on the characteristics of the electricity load dataset,data pre-processing and feature selection takes *** paper describes the results of a controlled experiment to study the importance of feature *** LSTMs model and SVM model are applied to one-hour ahead load forecasting and one-day ahead peak and valley load *** predictive accuracy of these models are calculated based on the error between the actual and predicted loads,and the runtime of the model is *** results show that the LSTMs model have a higher prediction accuracy when the load data is ***,the overall performance of the SVM model is better when the load data used to train the model is insufficient and the time cost is prioritized.

关键词： Deep neural network Model assessment Short-term load forecasting Feature selection Support Vector Machines Artificial Neural Networks Long Short-term Memory Networks Demand response

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Development of a Soft Actor Critic deep reinforcement learning approach for harnessing energy flexibility in a Large Office building

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能源与人工智能(英文) 2021年第3期5卷 228-241页

作者： Anjukan Kathirgamanathan eleni mangina Donal P.Finn School of Mechanical and Materials Engineering University College DublinIreland UCD Energy Institute O’Brien Centre for ScienceUniversity College DublinIreland School of Computer Science University College DublinIreland

This research is concerned with the novel application and investigation of‘Soft Actor Critic’based deep reinforcement learning to control the cooling setpoint(and hence cooling loads)of a large commercial building to harness energy *** research is motivated by the challenge associated with the development and application of conventional model-based control approaches at scale to the wider building *** Actor Critic is a model-free deep reinforcement learning technique that is able to handle continuous action spaces and which has seen limited application to real-life or high-fidelity simulation implementations in the context of automated and intelligent control of building energy *** control techniques are seen as one possible solution to supporting the operation of a smart,sustainable and future electrical *** research tests the suitability of the technique through training and deployment of the agent on an EnergyPlus based environment of the office *** agent was found to learn an optimal control policy that was able to minimise energy costs by 9.7%compared to the default rule-based control scheme and was able to improve or maintain thermal comfort limits over a test period of one *** algorithm was shown to be robust to the different hyperparameters and this optimal control policy was learnt through the use of a minimal state space consisting of readily available *** robustness of the algorithm was tested through investigation of the speed of learning and ability to deploy to different seasons and *** was found that the agent requires minimal training sample points and outperforms the baseline after three months of operation and also without disruption to thermal comfort during this *** agent is transferable to other climates and seasons although further retraining or hyperparameter tuning is recommended.

关键词： Deep Reinforcement Learning(DRL) Building energy flexibility Soft Actor Critic(SAC) Machine learning Smart grid

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