|关键词||建筑能耗 神经网络 回声状态网 自适应动态规划 预测 分类 优化|
With rapid economic development and continuous social progress, human demands for energy resources have been constantly growing. Due to increasingly serious global energy shortage, countries throughout the world are paying great attention to the issue of energy management and especially building energy saving. At present, many countries have established building energy consumption management systems and carried out energy consumption subentry measurements, which have produced an increasing amount of energy consumption data, however, little useful information has been extracted from these data to facilitate building energy saving.
As an important part of computational intelligence, neural networks (NNs) can learn from a large amount of data, and realize function approximation, system identification, pattern classification, etc. Echo state networks (ESNs) are a new type of recurrent neural networks (RNNs) with a concise training process, which can avoid complex training algorithms, local minima and other problems of traditional neural networks. Adaptive dynamic programming (ADP) is a neural network based intelligent control method, which has strong ability of learning and optimization, and can perfectly solve the optimal control problem of complex nonlinear systems. Therefore, the two neural network based methods above are investigated in this paper to conduct analysis and optimization of building energy consumption.
The main works and contributions of this paper are concluded in the following aspects:
1. Based on the data of building energy consumption, an ESN based energy consumption prediction method for office buildings is developed to predict energy consumptions from sockets, lights and air-conditioners in office buildings. As regards the optimization of reservoir topology in ESNs, several simplified reservoir topologies are developed, and ESNs with the different reservoir topologies are applied to the prediction of office building energy consumption, where the performance of different topologies is compared. Experimental analysis shows that the developed reservoir topologies can achieve comparable prediction performance to traditional ESNs, and parameter sensitivity analysis on the topologies demonstrates that all the developed topologies present fairly strong robustness.
2. In accordance with the energy consumption models of rooms in office buildings, an ESN based room classification method for office buildings is developed to classify rooms into different categories. In the developed method, first the energy consumptions from sockets, lights and air-conditioners are modeled by three ESNs, respectively, and then based on the models, rooms are classified into office rooms, computer rooms, storage rooms and meeting rooms by a fourth ESN according to the energy consumption characteristics of different rooms. The convergence of the developed classification algorithm is proved. Experimental analysis demonstrates that the developed method can achieve high classification accuracies, and compared with several traditional classification algorithms, the developed method has better classification capability.
3. Given the results of room classification, energy storage equipments, such as storage batteries, are introduced into the rooms, and an ADP based energy consumption management method for office buildings is developed to obtain the optimal battery control strategies according to real-time electricity rate and energy demand, so as to control and optimize the energy consumption in different categories of rooms, and thereby achieving energy management of office buildings. It is illustrated by experimental analysis that the developed method can obtain optimal battery control strategies for different categories of rooms, thus reducing the total energy cost. In addition, the developed method can achieve better effects of energy saving than other optimization algorithms.
4. With solar energy introduced as the source of renewable energy, a Q-learning based renewable energy scheduling and battery control method for office buildings is developed to further optimize the energy consumption and reduce the energy cost. Two iterations are introduced in the developed method to approximate the optimal performance index function and optimal control in ADP algorithms, respectively. Experimental analysis focuses on offices in office buildings, and by implementing the developed method, the optimal renewable energy scheduling and battery control schemes of an office in different seasons are obtained, and the energy costs are further reduced. Moreover, the developed method can also save more energy compared with several other optimization algorithms.
|石光. 基于神经网络的建筑能耗分析与优化[D]. 北京. 中国科学院大学,2017.|