Electrical load proﬁles of a particular region are usually required in order to study the performance of renewable energy technologies and the impact of different operational strategies on the power grid. Load proﬁles are generally constructed based on measurements and load research surveys which are capital and labour-intensive. In the absence of true load proﬁles, synthetically generated load proﬁles can be a viable alternative to be used as benchmarks for research or renewable energy investment planning. In this paper, the feasibility of using publicly available load and weather data to generate synthetic load pro-ﬁles is investigated. An artiﬁcial neural network (ANN) based method is proposed to synthesize load pro-ﬁles for a target region using its typical meteorological year 2 (TMY2) weather data as the input. To achieve this, the proposed ANN models are ﬁrst trained using TMY2 weather data and load proﬁle data of neighbouring regions as the input and targeted output. The limited number of data points in the load proﬁle dataset and the consequent averaging of TMY2 weather data to match its period resulted in lim-ited data availability for training. This challenge was tackled by incorporating generalization using Bayes-ian regularization into training. The other major challenge was facilitating ANN extrapolation and this was accomplished by the incorporation of domain knowledge into the input weather data for training. The performance of the proposed technique has been evaluated by simulation studies and tested on three real datasets. Results indicate that the generated synthetic load proﬁles closely resemble the real ones and therefore can be used as benchmarks.
|Number of pages||11|
|Journal||International Journal of Electrical Power & Energy Systems|
|Early online date||29 Mar 2014|
|Publication status||E-pub ahead of print - 29 Mar 2014|