A Full-Fledged, Multi-Agent System Representing the Architecture of Smart Cities by Balancing Energy With Optimal Electricity Forecasting, Integrating Individual Comfort, and Extracting Financial Gains

Abstract

Moving from smart homes to smart cities is a complex but essential task to consider. Setting up a modern smart city has many problems, such as unstable power generation systems, a lack of integration of demand-side loads, low profits, more pollution, and agents that cannot communicate quickly and smartly. Our model has the following steps to deal with all of these problems. It performs consumption forecasting through GNN and the Smart Hybrid model (LSTM + GNN) to get optimal forecasting results. The smart hybrid model outperformed the LSTM model by 1.14% in MAPE. The LSTM and Smart Hybrid models have MAPE values of 0.0787 and 0.0776, respectively. This infrastructure uses GNN, Smart Hybrid Model, MOPnP, MOPPnP, and knapsack algorithms to maximize personal comfort, lower costs, profit the community through transaction agents, reduce unwanted peaks by shifting loads, and use an intelligent and robust structure for agent communication. The integration of renewables, optimum consumption forecasts, adjusting consumer comfort, decentralized energy market structure and formulated algorithms have combined to reduce monthly electricity expenses by up to 94%. For the four trade setups that were examined, the unrealized PnL rates were +116%, +78%, +71.631%, and +250%, in that order.

Keywords

• Energy forecasting
• neural network
• load forecasting
• smart grids
• energy balancing
• multi-microgrid