Cai-Xin Sun

Bio: Cai-Xin Sun is an academic researcher from Chongqing University. The author has contributed to research in topics: Rough set. The author has an hindex of 1, co-authored 2 publications receiving 198 citations.

BP neural network with rough set for short term load forecasting

Abstract: Precise Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors to develop the accuracy of predictions. Through attribute reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased. Using load time series from a practical power system, we tested the performance of RSBP by comparing its predictions with that of BP network.

Short term load forecasting using neural network with rough set

Abstract: Accurate Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors in order to develop the accuracy of predictions. Through attribution reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased. Using load time series from a practical system, we tested the accuracy of forecasting in specific days with comparison.

Energy models for demand forecasting—A review

Abstract: Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting

Abstract: As a clean and renewable energy source, wind energy has been increasingly gaining global attention. Wind speed forecast is of great significance for wind energy domain: planning and design of wind farms, wind farm operation control, wind power prediction, power grid operation scheduling, and more. Many wind speed forecasting algorithms have been proposed to improve prediction accuracy. Few of them, however, have studied how to select input parameters carefully to achieve desired results. After introducing a Back Propagation neural network based on Particle Swam Optimization (PSO-BP), this paper details a method called IS-PSO-BP that combines PSO-BP with comprehensive parameter selection. The IS-PSO-BP is short for Input parameter Selection (IS)-PSO-BP, where IS stands for Input parameter Selection. To evaluate the forecast performance of proposed approach, this paper uses daily average wind speed data of Jiuquan and 6-hourly wind speed data of Yumen, Gansu of China from 2001 to 2006 as a case study. The experiment results clearly show that for these two particular datasets, the proposed method achieves much better forecast performance than the basic back propagation neural network and ARIMA model.

Forecasting methods in energy planning models

Abstract: Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs.