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.

Energy models for demand forecasting—A review

We further contribute to the properties of fuzzy soft sets as defined and studied in the work of Maji et al. (2001), Roy and Maji (2007), and Yang et al. (2007) and support them with examples and counterexamples. We improve Proposition 3.3 by Maji et al., (2001). Finally we define arbitrary fuzzy soft union and fuzzy soft intersection and prove DeMorgan Inclusions and DeMorgan Laws in Fuzzy Soft Set Theory.

On fuzzy soft sets

Soft set theory and uni–int decision making

In this work, we define soft matrices and their operations which are more functional to make theoretical studies in the soft set theory We then define products of soft matrices and their properties We finally construct a soft max-min decision making method which can be successfully applied to the problems that contain uncertainties

Soft matrix theory and its decision making

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.

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

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.

BP neural network with rough set for short term load forecasting

A soft probability based dynamic ensemble classification method is proposed.Soft probability covers classifiers selection and combination.Selecting classifiers based on Type I and II error can minimize the risk.Combing different classifiers for the testing samples is more reasonable.Selective ensembles for credit scoring are a promising research field. In recent years, classification ensembles or multiple classifier systems have been widely applied to credit scoring, and they achieve significantly better performance than individual classifiers do. Selective ensembles, an important part of this group of systems, are a promising field of research. However, none of them considers the relative costs of Type I error and Type II error for credit scoring when selecting classifiers, which bring higher risks for the financial institutions. Moreover, earlier dynamic selective ensembles usually select and combine classifiers for each test sample dynamically based on classifiers performance in the validation set, regardless of their behaviors in the testing set. To fill the gap and overcome the limitations, we propose a new dynamic ensemble classification method for credit scoring based on soft probability. In this method, the classifiers are first selected based on their classification ability and the relative costs of Type I error and Type II error in the validation set. With the selected classifiers, we combine different classifiers for the samples in the testing set based on their classification results to get an interval probability of default by using soft probability. The proposed method is compared with some well-known individual classifiers and ensemble classification methods, including five selective ensembles, for credit scoring by using ten real-world data sets and seven performance indicators. Through these analyses and statistical tests, the experimental results demonstrate the ability and efficiency of the proposed method to improve prediction performance against the benchmark models.

Dynamic ensemble classification for credit scoring using soft probability

In this paper, an appropriate definition of soft information is put forward. In order to discover soft sets application in recognizing soft information patterns, firstly analyze the basic definition of soft sets, make use of table to describe soft sets and give the conception of soft sets reduction according to the characters of soft sets. Then, the method is designed that recognizing soft information patterns by establishing the information table based on soft sets theory; at the same time the solutions are proposed corresponding to the different recognition vectors. This method with good maneuverability can operate collaterally and by batch so as to release the difficulty and complexity in information analysis by some extents.

Recognition for soft information based on the theory of soft sets

As the ensemble methods achieve significantly better performances than individual models do, they have been widely applied to credit scoring. However, most of them employ a static combiner to combine base classifiers, which do not consider the base classifiers’ characters and their dynamic classification ability. Though some dynamic ensemble methods are proposed, they need to produce a large number of base classifiers or employ a fixed combiner, which limit the generality of the ensemble methods. In this paper, we propose a new dynamic weighted ensemble method for credit scoring. Markov Chain is employed to model the change of each classifier’s classification ability and build a dynamic weighted trainable combiner, which dynamically assign weights to the base classifiers for each sample in the testing set. Through eight credit data sets from the real world, the experimental study demonstrates the ability and efficiency of the dynamic weighted ensemble method to improve prediction performance against the benchmark models, including some well-known individual classifiers and dynamic ensemble methods. Moreover, the proposed method can effectively decrease the misclassification cost, which can reduce risks for the financial institutions.

Bo Zhong

Papers

BP neural network with rough set for short term load forecasting

Dynamic ensemble classification for credit scoring using soft probability

Recognition for soft information based on the theory of soft sets

Dynamic weighted ensemble classification for credit scoring using Markov Chain

Sustainability or continuous damage: A behavior study of prosumers’ electricity consumption after installing household distributed energy resources