Dynamic energy prices for residential users based on Deep Learning prediction models of consumption and renewable generation

Abstract

New demand-side management models have emerged as a result of rising energy prices, the development of artificial intelligence, and the rise of prosumers. The purpose of this research is to use deep learning techniques to predict the energy production and demand of a prosumer network to determine dynamic prices for the local market. Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) were two methods that were taken into consideration for forecasting consumer demand and wind and solar energy generation. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) were used to compare the various approaches. The results demonstrated that GRU, with 0.0273, 0.0158, and 49.8 in RMSE, MAE, and MAPE respectively, is the best method for predicting energy generation and consumption in our datasets. Demand management system dynamic prices were calculated on an hourly basis using input from energy generation and demand forecasts. Finally, an optimization method was developed for establishing the energy planning.

The Influence of the Global Energy Crisis on Energy Efficiency: A Comprehensive Analysis

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Impact of Economic Awareness on Sustainable Energy Consumption: Results of Research in a Segment of Polish Households

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“…These could involve providing financial assistance, subsidies, or rebates for energy-efficient upgrades to low-income households, as well as investing in energy efficiency programs in affordable housing developments. Additionally, improving access to energy literacy programs and financial education resources in schools and community centers can empower individuals from all socio-economic backgrounds to make more informed decisions about energy consumption and budget management [4,[14][15][16][17][18].…”

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Demand-Side Management Method for Households with Self-Generation and Storage of Electricity

“…Over the past years, numerous research studies have been carried out to analyse the actual and future supply and demand energy patterns in the residential sector [31][32][33][34][35][36]. Research works focus on the characterisation of the energy profile of households (apartments and single-houses), consumers' preferences, and impact over the grid management [37][38][39] in order to facilitate utilities´planning activities while benefitting the consumer with a reduction in their energy bills.…”