Energy efficiency is crucial in helping to tackle the climate crisis - 26% of UK carbon dioxide emissions come from homes. The building environment is responsible for 40% of the UK's carbon footprint (www.ukgbc.org/climate-change and the UK has one of the oldest and least efficient housing sectors in Europe (Green Finance Institute - Tooling up the Green Homes Industry).COP 26 in Glasgow in 2021 highlighted the increase in carbon dioxide emissions from human activity and the need to address this as a global priority. E-MAP initial feasibilty study successfully assessed the technical viability of implementing a predictive pre-design energy consumption predictive model by utilizing advanced machine-learning algorithms with its potential benefits for the construction industry and sustainable building practices. With the recent Government Housing Initiative - Future Home Standards (FHS 2025), the aim is to focus on the decarbonising of heating and with less reliance on assumptions and more on empirical data to make energy consumption modelling more accurate. E-MAP aims to significantly speed up this work and add a greater degree of accuracy in the energy consumption predictions.E-MAP will also support the government's challenge of substantially reducing the energy use of new buildings. We will aim to focus on the further development and business exploitation of the E-MAP Artificial Intelligence (AI) model to accurately and efficiently predict energy consumption at the design stage of buildings, without requiring extensive building feature details and at the same time reducing carbon emissions.

The high cost of importation and capital costs of imported renewable energy systems are restricting access to products that would give consumers the options they need to adopt affordable solutions that suit their needs. There is thus a need for lowering costs of such systems through local innovation and production, for example.This project aims to develop a solar+wind powered PowerBox that can power basic appliances in the homes of low and medium class and micro and small businesses. A key challenge of this project is the product's affordability to the targeted group. This project builds on the result of engagement on an existing prototype we created from past research developments works. Feedback from engagement includes the need for increased power output without losing its affordability factor and a more practical body design.

Energy efficiency is crucial in helping to tackle the climate crisis - 26% of UK carbon dioxide emissions come from homes. The building environment is responsible for 40% of the UK's carbon footprint ([www.ukgbc.org/climate-change][0]) and the UK has one of the oldest and least efficient housing sectors in Europe (Green Finance Institute - Tooling up the Green Homes Industry).COP 26 in Glasgow in 2021 highlighted the increase in carbon dioxide emissions from human activity and the need to address this as a global priority. This project(E-MAP) focuses on conducting a feasibility study for the development of an Artificial Intelligence (AI) model that can accurately and efficiently predict energy consumption at the design stage of buildings, without requiring extensive building feature details. E-MAP aims to assess the technical viability of implementing such a model by utilizing advanced machine-learning algorithms with its potential benefits for the construction industry and sustainable building practices.E-MAP will also support the government's challenge of substantially reducing the energy use of new buildings. [0]: http://www.ukgbc.org/climate-change

The AlphaGenesis Consultancy is seeking to Facilitate, Innovate and Operate new business management service models for Low Carbon Networks in UK energy communities.