An evolutionary mixture-of-experts approach to data cleansing and load prediction

Load prediction is an integral part of operation, management and planning of power grids. As the electrical grid is transformed into a Smart Grid, the complexity and necessity of load prediction increases due to two main reasons: 1) the generation of power is distributed, often from intermittent sources, and 2) the utilization of existing infrastructure needs to be optimized. Load prediction must therefore be performed at the distribution and microgrid level. Load prediction on transmission level has been thoroughly investigated, however the same approach is not necessarily applicable as load prediction is performed on a smaller set of users (i.e. increasingly local scale), due to the increased volatility, noise and nonlinearity of the load data itself and miscellaneous local exogenous variables that influence the load in a way that is averaged out on transmission level load data. Such “local” models must therefore be tuned accordingly to deal with the varying dynamics of the underlying signal. Tuning the parameters of a given model is normally done with intuition and experience, however we employ an evolutionary approach to automatically tune the parameters of a given model. An important part of load prediction is to include other factor that influence load consumption locally, e.g. weather and temperature. As mentioned above, the more “local” a data series is, the more prone it is to noise. We therefore perform data cleansing as a pre-processing stage of the load history and other driver data. Data cleansing consists of outlier detection and removal. The genetic algorithm finds these parameters in tandem with those of the prediction algorithms. The cleansing and prediction algorithms are therefore tuned with respect to both each other and the dataset, whose resolution varies from transmission level down to a few hundred residential loads. Five different prediction models are used: autoregression, indexed autoregression, wavelets, Echo State Networks and CaseBased Reasoning. Each of these models predicts 24 hours into the future, based on historical load and temperature time series. In accordance with previous work, we compare each model with 24 specialized instances of itself, where each instance is trained to predict one of the 24 hours in the following day. With all the models evolved, we employ a mixture of experts approach as a final stage of the load prediction framework. The models’ performance during a validation period determine their weighting, which is employed during the final test period. The results show that the mixture of experts approach consistently beats any of the evolved models on their own, and furthermore that the 24 hour specialized instances of the different models do not outperform the single instantiation of a given model. *** To be confirmed, so far only seen for AR model. Maybe not even there *** An important reason for using an evolutionary approach was to fully automate the parameter setting when changing the granularity of the data set (i.e. moving from transmission level loads to distribution level to single household loads). We find that the parameters found at each level differ greatly, which justifies the use of a genetic algorithm in order to find the optimal parameters, an approach that also alleviates the need for intimate knowledge and experience with each model employed.