Load profiles are indispensable in the decision making process of power transmission and distribution companies. Increasing levels of customer-side renewable generation and electric transport will alter the nature of load profiles significantly. Traditional methods relying on historical data will not be suitable for modelling the increasingly complex power networks of the future. In this paper the feasibility of synthesising future load profiles under increasing levels of photovoltaic (PV) generation and electric vehicles (EV) is investigated using an artificial neural network (ANN) based method, trained with publically available data. The performance of the proposed method is evaluated by using a case study developed for a targeted region in the UK. A comparison of results from the ANN model against those using Multiple Linear Regression (MLR) demonstrates the superior performance of ANN over MLR as well as proves the viability of ANN to synthesise future load profiles.
|Number of pages||5|
|Publication status||Published - 12 Apr 2018|
|Event||5th International Conference on Renewable Energy Generation and Applications - United Arab Emirates, Al Ain, United Arab Emirates|
Duration: 28 Feb 2018 → 28 Feb 2018
|Conference||5th International Conference on Renewable Energy Generation and Applications|
|Country/Territory||United Arab Emirates|
|Period||28/02/18 → 28/02/18|