Improving Load Forecasting and Renewable Energy Management for Green Computing using FIS, PSO and Catfish

2019 ◽  
Author(s):  
Balbir Bob Gill ◽  
Maninder Singh ◽  
Ajay Kakkar
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Load Forecasting ◽  
Green Computing

scholarly journals Evaluation of Sequence Learning Models for Large Commercial Building Load Forecasting

2019 ◽  
Author(s):  
Cristina Nichiforov ◽  
Grigore Stamatescu ◽  
Iulia Stamatescu ◽  
Ioana Fagarasan
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Energy Management ◽  
Smart Grids ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Quality Data

Buildings have started to play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large scale energy management strategies from the supply side to the consumer side. When the buildings integrate local renewable energy generation in the form of renewable energy resources they become prosumers and this reflects into additional complexity into the operation of the interconnected complex energy systems. A class of methods of modelling the energy consumption patterns of the building have recently emerged as black-box input-output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produces by non-deterministic processes underlying the energy consumption. We present an application of a class of neural networks, namely deep learning techniques for time series sequence modelling with the goal of accurate and reliable building energy load forecasting. The Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects and are considered suitable for further used in future in situ energy management at the building and neighbourhood levels.

scholarly journals Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 189 ◽  
Author(s):  
Cristina Nichiforov ◽  
Grigore Stamatescu ◽  
Iulia Stamatescu ◽  
Ioana Făgărăşan
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Energy Management ◽  
Smart Grids ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Quality Data

Buildings play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large-scale energy-management strategies from the supply side to the consumer side. When buildings integrate local renewable-energy generation in the form of renewable-energy resources, they become prosumers, and this adds more complexity to the operation of interconnected complex energy systems. A class of methods of modelling the energy-consumption patterns of the building have recently emerged as black-box input–output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produced by nondeterministic processes underlying energy consumption. We present an application of a class of neural networks, namely, deep-learning techniques for time-series sequence modelling, with the goal of accurate and reliable building energy-load forecasting. Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects, and are considered suitable for further use in future in situ energy management at the building and neighborhood levels.

scholarly journals The Energy System of an Ecovillage: Barriers and Enablers

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 682
Author(s):  
Zita Szabó ◽  
Viola Prohászka ◽  
Ágnes Sallay
Keyword(s):  
Renewable Energy ◽  
Spatial Structure ◽  
Energy Management ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Energy System ◽  
Energy Sources ◽  
Energy Management System ◽  
Production And Consumption ◽  
Ecologically Sustainable

Nowadays, in the context of climate change, efficient energy management and increasing the share of renewable energy sources in the energy mix are helping to reduce greenhouse gases. In this research, we present the energy system and its management and the possibilities of its development through the example of an ecovillage. The basic goal of such a community is to be economically, socially, and ecologically sustainable, so the study of energy system of an ecovillage is especially justified. As the goal of this community is sustainability, potential technological and efficiency barriers to the use of renewable energy sources will also become visible. Our sample area is Visnyeszéplak ecovillage, where we examined the energy production and consumption habits and possibilities of the community with the help of interviews, literature, and map databases. By examining the spatial structure of the settlement, we examined the spatial structure of energy management. We formulated development proposals that can make the community’s energy management system more efficient.

Sign in / Sign up

Export Citation Format

Share Document