The existence of sunlight, air and other resources on earth must be used in an appropriate way for human welfare while still protecting the environment and its living creatures. The exploitation of sunlight and air as a substantial Renewable Energy (RE) source is an important research and development domain over past few years. The present and future overtaking in RE mainly comprises of (i) the development of novel technology for optimum production from the available natural resources (ii) environmental awareness, and (iii) the better management and distribution system. Like other domains (food, health, accommodation, safety, etc.), Artificial Intelligence (AI) could assist in achieving the future goals of the RE. Statistical and biologically inspired AI methods have been implemented in several studies to achieve common and future aims of the RE. The present study summarizes the review of reviews and the state-of-the-art research outcomes related to wind energy, solar energy, geothermal energy, hydro energy, ocean energy, bioenergy, hydrogen energy, and hybrid energy. Particularly, the role of single and hybrid AI approaches in research and development of the previously mentioned sources of RE will be comprehensively reviewed.

Renewable energy: Present research and future scope of Artificial Intelligence

Solar energy is a clean renewable energy source and availability of solar resources at a particular location depends on the local meteorological parameters. In the present study, prediction models using artificial neural networks (ANN) are developed by varying the meteorological parameters from one to six. A two year database of daily global solar radiation (GSR), daily minimum temperature (Tmin), daily maximum temperature (Tmax), difference of daily maximum and minimum temperature (DT), sunshine hours (S), theoretical sunshine hours (So) and extraterrestrial radiation (Ho) have been used to train the ANN. Six ANN models are developed (ANN-1 to ANN-6) with 32 possible combinations of inputs and are used to train the network to identify the best combination of inputs to estimate the monthly mean daily GSR accurately. All the models are validated and the performance of the models are analyzed by using the statistical tools. Out of the six ANN models with all the possible combination of input variables, ANN-2 and ANN-3 have given best prediction with the combinations of [DT, Ho] and [DT, Ho, So] respectively. The statistical tool Relative Root Mean Square Error (RRMSE) showed the least value of 3.96% with [DT, Ho] inputs. The ANN-1 trained with calculated approximate sunshine hours (Sa) has also shown high prediction accuracy. Sunshine based model and temperature based model are validated with ANN-1, ANN-2 and ANN-3 architectures. Results showed that the developed ANN models outperform the considered empirical models. The combination of [So, Ho] has produced excellent estimation, which are theoretical parameters and does not require any measured meteorological parameters. Superior performance is observed with less number of inputs which are readily available for any location.

Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study

Modeling wind power investments, policies and social benefits for deregulated electricity market – A review

An extended new approach for forecasting short-term wind power using modified fuzzy wavelet neural network: A case study in wind power plant

Abstract The development of distributed renewable energy, such as photovoltaic power and wind power generation, makes the energy system cleaner, and is of great significance in reducing carbon emissions. However, weather can affect distributed renewable energy power generation, and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy. Energy systems with high penetration of distributed renewable energy involve the high-dimensional, nonlinear dynamics of large-scale complex systems, and the optimal solution of the uncertainty model is a difficult problem. From the perspective of statistical machine learning, the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning. 

/pdf/planning-of-distributed-renewable-energy-systems-under-g8t5iim3.pdf

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

The paper discusses the possibilities of multi-criteria optimisation of a multi-layer perceptron (MLP) model applied to the short-term (intra- and next-day) wind power forecasting problem. The paper comprises two main parts: a theoretical background and study case using data (wind power production and historical weather forecast) obtained from two wind farms (at different power capacity levels). The problem stated in this paper is to formulate a method allowing for the estimation of a set of prediction models meeting the selected three model learning criteria: n BIAS , n MAE and n RMSE . The two-step NISE method has been used in order to estimate the non-dominated forecast evaluation set. The available data have been divided into three subsets for model learning, testing and validation. Than, a set of prediction model variants has been investigated considering different types of data subsets used for stopping the MLP learning process as well as calculating the forecast error. Additionally, different structures of MLP and learning algorithms have been analysed. Finally the paper is ended with a summary and conclusions.

Short-term electric energy production forecasting at wind power plants in pareto-optimality context

http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-ae761f2e-9b4f-4d73-bab3-8699e89c6b0a/c/wasilewski_i_in._wybrane_18_1_15.pdf.pdf

Wybrane zagadnienia rozwoju mikrosieci energetycznych w Polsce

The idea of the MGrid system for multicarrier energy microgrid designing and control is presented in the article. The first part of the work contains reasons for undertaking the problem of complex optimisation of utilising various energy forms on the operation area of a typical microgid. The planned system functionality and architecture is also presented. Conclusions concerning expected advantages as a consequence of using the proposed system are discussed.

Idea of MGrid System for Multicarrier Energy MicroGrid Designing and Control

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Short-term electric energy production forecasting at wind power plants in pareto-optimality context

Wybrane zagadnienia rozwoju mikrosieci energetycznych w Polsce

Idea of MGrid System for Multicarrier Energy MicroGrid Designing and Control