Improving the quality of load forecasts using smart meter data

For the operator of a power system, having an accurate forecast of the day-ahead load is imperative in order to guaranty the reliability of supply and also to minimize generation costs and pollution. Furthermore, in a restructured power system, other parties, like utility companies, large consumers and in some cases even ordinary consumers, can benefit from a higher quality demand forecast. In this paper, the application of smart meter data for producing more accurate load forecasts has been discussed. First an ordinary neural network model is used to generate a forecast for the total load of a number of consumers. The results of this step are used as a benchmark for comparison with the forecast results of a more sophisticated method. In this new method, using wavelet decomposition and a clustering technique called interactive k-means, the consumers are divided into a number of clusters. Then for each cluster an individual neural network is trained. Consequently, by adding the outputs of all of the neural networks, a forecast for the total load is generated. A comparison between the forecast using a single model and the forecast generated by the proposed method, proves that smart meter data can be used to significantly improve the quality of load forecast.