A Review on Deep Learning with Focus on Deep Recurrent Neural Network for Electricity Forecasting in Residential Building

Abstract

The rapid increase in urbanization has resulted in a significant rise in electricity consumption, which resulted in a wide gap between the amount of electricity generated and the consumer’s demand. Literature shows that 40% of the generated electricity is consumed by building sectors. To address the gap between demand and supply, there is a need to develop novel prediction models that adopt automated techniques, to dynamically predict buildings energy consumption. An efficient load forecast can assist in efficient power generation and distribution among users. Different Machine Learning techniques have been applied in future electricity consumption forecasts with the need for a more ideal solution. This paper reviews methods for building energy consumption forecasts that use Machine Learning algorithms like Artificial Neural Network, Deep Belief Network, Recurrent Neural Network, Elman Neural Network, Deep Recurrent Neural Network, Convolutional Neural Network and Nonlinear Autoregressive Network. The review explores existing research gaps and research directions for future work. Finally, a novel Deep Learning framework was suggested for future work on enhancing prediction performance and reliability using occupancy profile and distinct climatic scenarios based on Transfer Learning and LSTM algorithms (Trans-LSTM) for medium to long term electricity consumption forecast.