Machine Learning Techniques for Short-Term Electric Power Demand Prediction
نویسندگان
چکیده
Since several years ago, power consumption forecast has attracted considerable attention from the scientific community. Although there exist several works that deal with this issue, it remains open. The good management of energy consumption in HVAC (Heating, Ventilation and Air Conditioning) systems for large households and public buildings may benefit from a sustainable development in terms of economy and environmental preservation. In this paper, several Machine Learning techniques are evaluated and compared with a linear technique (Robust Multiple Linear Regression) and a näıve method. All methods have been applied to five buildings of the University of León (Spain), the results indicate nonlinear techniques outperform the linear one in most scenarios.
منابع مشابه
Short term electric load prediction based on deep neural network and wavelet transform and input selection
Electricity demand forecasting is one of the most important factors in the planning, design, and operation of competitive electrical systems. However, most of the load forecasting methods are not accurate. Therefore, in order to increase the accuracy of the short-term electrical load forecast, this paper proposes a hybrid method for predicting electric load based on a deep neural network with a...
متن کاملShort term load forecast by using Locally Linear Embedding manifold learning and a hybrid RBF-Fuzzy network
The aim of the short term load forecasting is to forecast the electric power load for unit commitment, evaluating the reliability of the system, economic dispatch, and so on. Short term load forecasting obviously plays an important role in traditional non-cooperative power systems. Moreover, in a restructured power system a generator company (GENCO) should predict the system demand and its corr...
متن کاملA Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting
Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM), which combines k-Nearest Neighbor (KNN) and Ex...
متن کاملWind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data
Wind Power Ramp Events (WPREs) are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle...
متن کاملShort Term Load Forecasting Using Empirical Mode Decomposition, Wavelet Transform and Support Vector Regression
The Short-term forecasting of electric load plays an important role in designing and operation of power systems. Due to the nature of the short-term electric load time series (nonlinear, non-constant, and non-seasonal), accurate prediction of the load is very challenging. In this article, a method for short-term daily and hourly load forecasting is proposed. In this method, in the first step, t...
متن کامل