Abstract
In this paper, the historical power load data from the National Electricity Market (Australia) is used to analyze the characteristics and regulations of electricity (the average value of every eight hours). Then, considering the inverse of Euclidean distance as the weight, this paper proposes a novel short-term load forecasting model based on the weighted k-nearest neighbor algorithm to receive higher satisfied accuracy. In addition, the forecasting errors are compared with the back-propagation neural network model and the autoregressive moving average model. The comparison results demonstrate that the proposed forecasting model could reflect variation trend and has good fitting ability in short-term load forecasting.
Highlights
Short-term load forecasting is used to forecast the power loads in the coming months, weeks, or even shorter, with greater accuracy than long-term load forecasting
Classical deterministic theories are mainly applied to conduct the traditional short-term load forecasting. Such as time series method [3], back-propagation neural network (BPNN) model [4], gray model [5,6], and support vector regression [7,8,9], etc. These methods are widely adopted, there are still some outstanding problems, for example, (1) it is difficult to simulate the relationships between the variables affecting the electricity loads and the loads themselves by accurate mathematical model; (2) the forecasting accuracy requires improvements; (3) the forecasting effect is not satisfied; and (4) the real situation of the electricity load cannot be reflected in real time
This paper proposes a short-term load forecasting model based on the new parametrization of the W-k-nearest neighbor (K-NN) algorithm so that it is adapted to China patterns: (1) According to a known sample set, forecast the electricity loads at a certain time; (2) calculate the Euclidean distance using its proximity data, the reciprocal of the calculated distance is used to determine the weight for each data point; (3) the closer the distance, the greater the weight, the data points can be better classified and the short-term load can be better forecasted
Summary
Short-term load forecasting is used to forecast the power loads in the coming months, weeks, or even shorter, with greater accuracy than long-term load forecasting. Thereafter, several researchers have considered empowering weight for each nearest neighbor [19], for instance, Chen and Hao [20] proposed a support vector machine (SVM)-based weighted K-NN algorithm to effectively predict stock market indices by using support vector machines to obtain the associated weight for each feature Their forecasting results are better than other models. The weights are calculated distance-based on Gaussian kernel to measure the similarity between test sample and each training sample Their results demonstrate that the proposed algorithm could reach a higher recognition rate than other existing alternative models. Propose the simultaneous weighting of attributes and neighbors (SWAN) to improve the classification accuracy, by using an evolutionary computation technique to adjust the contribution of the neighbors and the significance of the features of the data Their results demonstrate that the proposed SWAN is superior to other alternative weighted K-NN methods.
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