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Project Proposal: Smart Home Energy Management and Trading System

1. Project Title: Smart Home Energy Management System with Multi-Agent Collaboration for Renewable Energy Production, Consumption Prediction, and Peer-to-Peer Energy Trading

2. Background and Motivation: The global energy landscape is rapidly transitioning towards renewable energy sources to combat climate change and reduce carbon footprints. In this context, smart homes equipped with renewable energy systems such as solar panels and wind turbines are gaining prominence. However, challenges remain in optimizing energy production, managing consumption, and facilitating efficient peer-to-peer (P2P) energy trading among neighbors.

Multi-agent systems (MAS) offer a promising framework to address these challenges. By deploying autonomous agents with specific roles, the system can dynamically adapt to varying energy demands, optimize resource usage, and enable seamless energy trading. This project aims to design and implement a MAS for smart homes to enhance energy efficiency and foster a collaborative energy ecosystem.

3. Objectives:

1. (30% Mark) Develop the mathematical model for each agent. In your project, you should have at least five agents as follows:

· Prediction Agent: This agent must be able to predict the energy production and consumption for a house, determine the demand or surplus on an hourly basis.

· Demand Response Agent: This agent is responsible for interacting with the energy grid agent to engage in the demand response and curtailment requests to balance supply demand of energy:

· Behavioral and Segmentation Agent: This agent is responsible for appliance-to-appliance prediction and classification to determine the priority of smart home usage of appliances

· Negotiation Agent: This agent is responsible for energy trading among the neighbors and the power grid. This agent implements state-of-the-art auction theory. Your design should prove all the properties of auction theory for algorithmic game theory design

· Facilitating Agent: This agent serves as the interface and coordination among the agents in the system. You should design the interface of this agent and set the properties of interaction.

2. 15% Mark Create a peer-to-peer energy trading platform to facilitate secure and efficient energy transactions among neighboring homes.

3. (15% Mark) Evaluate the system’s performance in terms of energy efficiency, prediction accuracy, and trading efficacy.

4. (30% Mark) Use Software Agent Development Environment (e.g. JADE) to develop the system. The methodology of designing the project should be Agile.

5. (10% Mark) The students should find datasets on their own. Smart home datasets should include appliance level energy consumption, datasets for solar or wind should include hourly energy production, dataset for power grid should include energy consumption at the lowest granular level 

4. Deliverables:

4.1 System Architecture: The proposed system comprises the following components:

· Renewable Energy Sources: Solar panels and/or wind turbines.

· Energy Storage: Batteries for storing surplus energy.

· Multi-Agent System (MAS) : Interaction model and protocol

· User Interface: Provides real-time insights and control options for homeowners.

4.2 Mathematical Model for each agent and proofs of auction theories

4.3 Predictive Modeling: Develop machine learning models, such as LSTM networks or hybrid approaches, to forecast short-term and long-term energy production and consumption based on historical data, weather conditions, and user behavior patterns.

4.4 Peer-to-Peer Trading Platform: Design a secure (e.g. blockchain-based) platform for secure and transparent energy transactions. You may use smart contracts to  automate trading processes based on predefined rules, ensuring fairness and efficiency.

5.4 Simulation and Testing: Simulate the system in a controlled environment using synthetic and real-world datasets. Evaluate the MAS’s performance under various scenarios, including peak demand, renewable energy surplus, and dynamic market conditions.

6. Expected Outcomes:

1. A functional prototype of a smart home energy management system powered by a multi-agent framework.

2. Accurate predictive models for energy production and consumption.

3. A secure and efficient P2P energy trading platform.

4. Quantitative analysis of the system’s benefits, including cost savings, energy efficiency, and reduced reliance on the main grid.