Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Experimental validation of the demand response potential of residential heating systems

To effectively exploit the potential of demand response, knowledge regarding the availability of flexibility is crucial. In this paper, the flexibility of residential heating systems is assessed using the results of the Dutch smart grid pilot PowerMatching City. Within this pilot a multi-objective multi-agent system is used to exploit the flexibility of residential heat pumps and micro-CHPs. To validate the practical flexibility, a data-driven approach is proposed, in which the measured load is used to evaluate the effect of the smart-grid control signal on load changes. As this effect is subjected to other variables as well, the load is considered as a function of time, weather circumstances and the control signal. Two types of load forecasting techniques are applied to model the response of the load to these variables: multiple regression and Artificial Neural Networks (ANNs). Both forecasters obtain comparable results regarding the available flexibility of the devices. However, the results indicate that ANNs are slightly better at capturing the non-linearity of flexibility

Experimental validation of the demand response potential of residential heating systems

To effectively exploit the potential of demand response, knowledge regarding the availability of flexibility is crucial. In this paper, the flexibility of residential heating systems is assessed using the results of the Dutch smart grid pilot PowerMatching City. Within this pilot a multi-objective multi-agent system is used to exploit the flexibility of residential heat pumps and micro-CHPs. To validate the practical flexibility, a data-driven approach is proposed, in which the measured load is used to evaluate the effect of the smart-grid control signal on load changes. As this effect is subjected to other variables as well, the load is considered as a function of time, weather circumstances and the control signal. Two types of load forecasting techniques are applied to model the response of the load to these variables: multiple regression and Artificial Neural Networks (ANNs). Both forecasters obtain comparable results regarding the available flexibility of the devices. However, the results indicate that ANNs are slightly better at capturing the non-linearity of flexibility

Experimental validation of the demand response potential of residential heating systems

To effectively exploit the potential of demand response, knowledge regarding the availability of flexibility is crucial. In this paper, the flexibility of residential heating systems is assessed using the results of the Dutch smart grid pilot PowerMatching City. Within this pilot a multi-objective multi-agent system is used to exploit the flexibility of residential heat pumps and micro-CHPs. To validate the practical flexibility, a data-driven approach is proposed, in which the measured load is used to evaluate the effect of the smart-grid control signal on load changes. As this effect is subjected to other variables as well, the load is considered as a function of time, weather circumstances and the control signal. Two types of load forecasting techniques are applied to model the response of the load to these variables: multiple regression and Artificial Neural Networks (ANNs). Both forecasters obtain comparable results regarding the available flexibility of the devices. However, the results indicate that ANNs are slightly better at capturing the non-linearity of flexibility