Machine Learning-Based Demand Response Optimization for Energy-Efficient Smart Cities

Abstract

Urbanisation and development of smart urban infrastructures have greatly risen the demand in electricity and this has posed problem in ensuring effective management of energy and grid stability. Conventional energy management techniques are in most cases not effective in managing dynamic consumption behaviour, and load fluctuations at peak loads in contemporary cities. Demand response (DR) has become an efficient strategy of enhancing energy efficiency since it facilitates the management of loads temporarily and urges consumers to respond to the peak levels of demand in electricity. It is proposed in this research to utilise a machine learning-smarter framework of energy demand response optimization, which enhances energy demand prediction and optimises the load schedule in smart city power systems. The framework proposed combines the latest machine learning algorithms (Long Short-Term Memory (LSTM), Random Forest (RF) and Artificial Neural Networks (ANN)) to predict the electricity consumption profile basing on past energy trends and context data. These demand response models can facilitate smart decisions in demand response which are helpful to peak load reduction and distribution of the energy. Measures of forecasting accuracy are measured on well-known prediction measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage error (MAPE) to determine the performance of the proposed models. According to the results of the experiment, the demand response approach based on machine learning can be seen to substantially enhance the prediction performance and allows engaging in efficient load balancing which translates to tangible changes in peak demand and total energy usage. The results indicate the possibilities of machine learning applications to facilitate energy-saving smart city infrastructures and help create intelligent, adaptive smart grid management structures.