FMP event - Optimising energy use and controlling costs in the built environment - Lord Rupert Redesdale

EMA - CEO, Energy Managers Association.

Business

Optimising Energy Use and Controlling Costs in the
Built Environment
15 October 2015
Rupert Redesdale
CEO, Energy Managers
Association

EMA Initiatives
• Low Energy Company (LEC) training programme
- to reduce the energy consumption of organisations
operating
within the UK by training their employees in energy
efficiency and
awareness
• Energy Efficiency and Policy Development
- DECC, DEFRA, European Commission
• Energy Savings Opportunity Scheme (ESOS) Lead
Assessor Register
- Four-step process in place to approve ESOS Lead
Assessors
• Energy Performance Contracting
- Guidance, Finance and Insurance

Issues
• Generating supply
• Security of supply
• Cost of supply
• Demand Response
• Smart grid
• Smart water grid

Thank you.
Lord Redesdale
www.theema.org.uk

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The energy is a vital input for the social and economic development of any nation. With increasing agricultural and industrial activities in the country, the demand for energy is also increasing. The increasing use of natural and renewable energy sources is needed to take the burden of our current dependency on fossil fuels. Development and analysis of renewable energy models helps utility in energy forecasting, planning, research and policy making. The wind power is a clean, inexhaustible, and almost a free source of energy. But the integration of wind parks with the power grid has resulted in many challenges for the utility in terms of commitment and control of power plants. As wind speed and wind direction fluctuate frequently, the accurate long-term and short-term forecasting of wind speed is important for ascertaining the wind power generation availability. To deal with wind speed forecasting, many methods have been developed such as physical method, which use lots of physical considerations to reach the best forecasting precision and other is the statistical method, which specializes in finding the relationship of the measured power data. Wind speed can be predicted by using time series analysis, artificial neural network, Kalman Filter method, linear prediction method, spatial correlation models and wavelet, also by using the support vector machines. In this paper, the SVM is used for day ahead prediction of wind speed using historical data of wind park. In this paper Support Vector Machine (SVM) results are compared with feedforward Backpropagation neural network. It is observed that the Mean Absolute Percentage Error (MAPE) by SVM method is around 7% and correlation coefficient is close to 1. This justifies the ability of SVM for wind speed prediction task than Backpropagation algorithm.

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