Load-forecasting model based on normalized Gaussian pLSA collaborative filtering
基于标准化高斯pLSA协同过滤的用电量预测模型

To some extent the existing long-term load-forecasting algorithms have their limitations because the variables influencing the output of the complex non-linear system are too many to be described. By combining the probabilistic Latent Semantic Analysis (pLSA) that can cluster random data into respective aspects and content-based collaborative filtering, a novel load forecasting model based on normalized Gaussian probabilistic latent semantic analysis collaborative filtering is proposed in order to avoid seeking and describing of the hidden variables mentioned above. Simulating experiments via MATLAB show that this method gains the advantage in accuracy over neural network and grey prediction.