Evaluation on feed-in tariff scheme for residential area based on artificial neural network projection

Abstract

Malaysia has introduced Feed-in Tariff (FiT) mechanism in 2011. This is accordance with Renewable Energy Act 2011 and Sustainable Energy Development Authority Act 2011. This mechanism is to promote the development and encouragement of renewable energy sector in Malaysia such as solar photovoltaic (PV), biomass, biogas, small hydro and geothermal. After 5 years of implementation in Malaysia, FiT mechanism has been know as an effective solution to make a monthly income from the energy produced from renewable sources. Hence, the residential area has started to install solar PV after the FiT was introduced. However, without taking any consideration the possibility of increament in electricity tariff, bank loan commitment, solar irradiation, increase in energy consumed and weather conditions, the existing FiT will not give an advantages to the customer. Therefore, the purpose of this project is to evaluate the FiT scheme for long term condition to residential area by using solar PV system as a renewable sources. This can be achieved by study the historical data of the electricity tariff, FiT rates, solar irradiation and energy consumption for residential house for 21 years. From the linear regression projection, it was projected that the electricity tariff will be increased around 140% from year 2015 to year 2035 for block tariff above 300kWh. In order to validate the data projection and analysis of the electricity tariff, an Artificial Neural Network (ANN) projection is also been used. The ANN analysis shown the FiT for PV system in residential area can give negative impact in long term condition.