Zongyun Song

Improved short-term load forecasting based on EEMD, Guassian disturbance firefly algorithm and support vector machine

Accurate short-term load forecasting plays a crucial role in electricity industry and market. In this study, a novel forecasting method based on Support Vector Machine (SVM) and Firefly Algorithm (FA) has been created to realize accurate and reliable load prediction. The performance of SVM highly depends on the selection of parameters, and Gaussian disturbance Firefly Algorithm (GDFA) proposed in this study can satisfy the necessary. Ensemble Empirical Mode Decomposition (EEMD) is employed to decompose the load data into sub-series with different frequency. This paper extracts daily maximum temperature, daily minimum temperature, wind speed, rainfall, day type, and the load one week before the forecasting day as input variables. Two cases are taken to verify the effective performance of GDFA compared with FA, as well as the superiority of EEMD-GDFA-SVM over other forecasting techniques in short-term load forecasting.

Distributed power generation programming with waste incineration generation and hybrid energy storage equipment

Under the current situation, distributed power generation becomes key solutions to the problems of environmental pollution and resources shortage that the electricity industry is facing. Rational power generation programming schemes will reduce energy consumption, and optimize energy structure on the premise of meeting the increasing requirements for electric energy. The multi-objective programming model was established to optimize economical and technical objectives of distributed generation system (DGS) containing waste incineration generation (WIG) and hybrid energy storage equipment (HESE), and then memorized-firefly algorithm (M-FA) is introduced to determine the model and installed capacity of various power generation units in distributed generation system. The case study demonstrated that the proposed programming model can obtain rational solutions with taking various objectives and constraints into consideration, and the M-FA shows the abilities of global search and better convergence in solving power generation programming problems, which will provide theoretic and practical references for the multi-objective programming problems of distributed generation system.

Efficiency differences of government investment projects: an application of a DEA and Tobit analysis

Previous studies have evaluated the investment efficiency of the government from a macro perspective, but few studies focus on specific projects with government investment. Therefore, this paper presents a method that combines data envelopment analysis and Tobit regression, which highlights the sources of funds, social benefits and influencing factors. It concludes that the overall efficiency of government investment projects (GIPs) is very high, but the excessive input and insufficient output are widespread and big improvement spaces exist; the public requirements rank first of all the influencing factors. This paper can provide references to guide the GIPs to gradually obtain better investment efficiencies.

Resources programming model of government investment project group based on improved firefly algorithm

Government investment project group (GIPG) plays an important role in increasing the welfare of the public, so the public is concerned about its investment efficiency, which can be reflected by resource programming level of GIPG. Previous research pays little attention to this field, and several problems exist: ordinary resource programming procedure is directly adopted; project management targets only focus on cost and schedule and the proposed optimisation algorithms are inapplicable. Hence, this research is carried out as follows: first, resource programming procedure of GIPG is organised. Second, the strategic targets of GIPG are determined based on the national, regional planning, and the projects targets are determined based on strategic targets, which ensure the coherence of national planning and projects targets. Third, the newly proposed optimisation algorithm-Gaussian firefly algorithm (GFA) is described and its effectiveness is validated through comparison with other algorithms. Finally, a case study demonstrates the effectiveness of resource programming method of GIPG.

Improved TOPSIS method for power distribution network investment decision-making based on benefit evaluation indicator system

Purpose The aim of this paper is to review the current situation and existing problem, establish investment benefits evaluation indicator system and introduce synthetic approach degree containing Hamming approach degree, Euclid approach degree and gray correlation degree to improve the shortage of Euclidean distance in traditional TOPSIS method, and the evaluation result is strengthened by multiplication rule. This paper aims to solve the distribution network investment decision-making problem and construct a comprehensive distribution network investment benefit indicator system, which is more suitable for China distribution network characteristics. Design/methodology/approach This study develops improved TOPSIS methods for decision maker in the power distribution network market and uses an example to prove its effectiveness and superiority in practice which can realize the combination of theory and practice. Findings The research shows that the investment evaluation indicator system built in present paper covers more investment benefit influencing factors (such as qualified rate of trunk cross-section, pass rate of N-1 lines), and the evaluation result obtained by improved TOPSIS method is more efficient and persuasive. Originality/value The study can help investors evaluate distribution network project more efficient, and make contribution to the choice of distribution cases with similar investment benefits.