Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions

Abstract

Demand Response (DR) is a fundamental aspect of the smart grid concept, as it refers to the necessary open and transparent market framework linking energy costs to the actual grid operations. DR allows consumers to directly or indirectly participate in the markets where energy is being exchanged. One of the main challenges for engaging in DR is associated with the initial assessment of the potential rewards and risks under a given pricing scheme. In this paper, a Genetic Algorithm (GA) optimisation model, using Artificial Neural Network (ΑΝΝ) power predictions for day-ahead energy management at the building and district levels, is proposed. Individual building and building group analysis is conducted to evaluate ANN predictions and GA-generated solutions. ANN-based short term electric power forecasting is exploited in predicting day-ahead demand, and form a baseline scenario. GA optimisation is conducted to provide balanced load shifting and cost-of-energy solutions based on two alternate pricing schemes. Results demonstrate the effectiveness of this approach for assessing DR load shifting options based on a Time of Use pricing scheme. Through the analysis of the results, the practical benefits and limitations of the proposed approach are addressed.

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

“…Time series [24,25,31,[34][35][36][37][38][39][40][41][42][43] Regression [28,[44][45][46][47] Econometrics [48][49][50][51][52] Expert systems and learning models Artificial neural network (ANN) [21,40,[53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69] Genetic programming (GP) [21,24,40,58,65,67,[69][70][71][72]…”

“…However, the initial objective was to propose a prediction model which is aimed to uncover main driving forces in order to approach to reliable extrapolations. ANN and GA were combined and implemented successfully by other previous researchers to predict future trends in the area of energy (for instance: [40,58,65,67,69]), but this paper presents a GNn with adjusted characteristics equipped with a purified input vector using data mining based pre-processing techniques. Data mining significantly helped to improve prediction accuracy and reliability.…”

Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model

“…In modelling building electricity use, Nizami and Garni [22] used a simple feed-forward ANN to relate the electrical demand to the number of occupants and weather data. Similarly, Kampelis et al [23] proposed an ANN power prediction for day-ahead energy management at the building and district levels. Wong et al [24] used an ANN to predict energy consumption for office buildings with day-lighting controls in subtropical climates; the outputs of the model include daily electricity usage for cooling, heating, and lighting.…”

Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation

“…In this paper, the authors selected Genetic Algorithm (GA) for optimization of MLP neural network architecture for Combined Cycle Power Plant (CCPP) electrical power output estimation. Similar research of energy management optimization at building and district levels is performed in [53] where the authors used ANN and GA. However, the authors in [53] used GA for optimization of ANN predictions, not for optimization of ANN architecture.…”

“…Similar research of energy management optimization at building and district levels is performed in [53] where the authors used ANN and GA. However, the authors in [53] used GA for optimization of ANN predictions, not for optimization of ANN architecture. Literature review offers many examples of using GA in analysis and optimization of various elements from many energy systems or its parts.…”