Hydropower is the major renewable energy contributor to the national grid in Srii
Lanka amounting to 48% of the total installed capacity. Further expansion of hydropower however, is|
limited due to environmental and resource constraints. Meanwhile the demand for electricity is'.
estimated to rise at an annual rate of 8% - 10% prompting the need to find alternative power options.^
The wind energy has been identified as a promising candidate to generate electricity in Sri Lanka. v|||
However for a reliable integration of wind energy, the volatile nature of wind has to be understood. ^
Accurate wind power forecasts are beneficial for wind plant operators, utility operators, and utility |S|
customers. Wind speed-time series data typically exhibit autocorrelation, which can be defined as the ||
degree of dependence on preceding values. This paper presents the state of the art wind power
forecasting technique and examines the use of a standard class of statistical time-series models to fjj
predict wind power output. Present study shows how an autoregressive model can serve as a ^
modelling and forecasting wind power generation in Puttalama area.
