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In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies and load demand (LD). An artificial neural network ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this paper, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management. [ABSTRACT FROM PUBLISHER]

The Philippine government has a continuing effort to reduce the utilization of fossil fuels which emit substantial amount of greenhouse gases. A suitable means of tapping energy which is clean and environmental friendly is sought. One such energy source is the micro-hydropower. In this paper, an artificial neural network (ANN) is used for predicting the generation of a micro-hydropower plant (MHPP) for a period of 1 month. A monthly forecasting of hydropower discharge was first conducted, which was then followed by the computation of the power output of MHPP. Results from simulation study show that a multi-layer perceptron (MLP) employing back propagation (BP) is successful in forecasting the discharge as exemplified by a less than 5% error for the test data. Results of statistical analysis confirm the validity of the forecasted discharge. MHPP supply (Estoperez and Nagasaka in Proceedings of HNICEM, Philippines, 2005) supplemented with the demand profile enables to determine the monthly reduced carbon emission of 46.8 tons. [ABSTRACT FROM AUTHOR]

Abstract: The first objective of this study is to determine the theoretical potential of solar irradiation in Indonesia by using artificial neural networks (ANNs) method. The second objective is to visualize the solar irradiation by province as solar map for the entire of Indonesia. The geographical and meteorological data of 25 locations that were obtained from NASA database are used for training the neural networks and the data from 5 locations were used for testing the estimated values. The testing data were not used in the training of the network in order to give an indication of the performance of the system at unknown locations. In this study, the multi layer perceptron ANNs model, with 9 inputs variables i.e. average temperature, average relative humidity, average sunshine duration, average wind speed, average precipitation, longitude, latitude, latitude, and month of the year were proposed to estimate the monthly solar irradiation as the output. Statistical error analysis in terms of mean absolute percentage error (MAPE) was conducted for testing data to evaluate the performance of ANN model. The best result of MAPE was found to be 3.4% when 9 neurons were set up in the hidden layer. As developing country and wide islands area, Indonesia has the limitation on the number of meteorological station to record the solar irradiation availability; this study shows the ANN method can be an alternative option to estimate solar irradiation data. Monthly solar mapping by province for the entire of Indonesia are developed in GIS environment by putting the location and solar irradiation value in polygon format. Solar irradiation map can provide useful information about the profile of solar energy resource as the input for the solar energy system implementation. [Copyright &y& Elsevier]

Abstract: This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P&O) algorithm dispositive. [ABSTRACT FROM AUTHOR]

Abstract The solar radiation fluctuation occurs at practically anywhere on the earth. When a solar thermal power generation system is designed for the areas with considerable solar radiation fluctuation, the collector tube exit temperature becomes more difficult to predict and requires significant calculation time. This paper presents a fast and accurate transient thermal prediction method to predict the parabolic trough collector tube exit temperature. In this work, an artificial neural network (ANN) is combined with the principle of superposition. ANN is used to predict the exit temperature rise caused by a single heat flux pulse in the first step of the proposed methodology, while superposition is used to predict the from multiple heat flux pulses in the second step. Limited cases of conjugate heat transfer analytical results by the finite element method (FEM) are used to train the ANN. The one-day exit fluid temperature from 7 a.m. to 6 p.m. is predicted within 1 min of computational time with mean absolute deviation less than 2 K. The exit fluid temperature of the collector tube for one year operation can be predicted in less than 6 h. Because fluid velocity is included in the input parameters, the proposed methodology is especially useful for flow control simulations where a constant exit temperature is targeted. Through this, the optimum performance of collector tube under multiple radiation conditions can be assessed during an early design phase of parabolic solar trough systems. The predicted results can be used for initial system planning, heat balance analysis, and system design. Since the method shows good prediction capability under the fluctuating solar radiation as well as the stable solar radiation, it is applicable to be used for designing the parabolic trough technology at any weather conditions in the world. Highlights • A prediction method for the exit fluid temperature of a parabolic trough collector tube was proposed. • The proposed prediction method combines ANN and superposition principle in the prediction algorithm. • Less than 1 min computation time is required to predict the one-day exit fluid temperature. • The overall MAD is relatively small, which is less than 2 K. • The method can be used to replace the conventional FEM for an annual yield assessment during initial design stage. [ABSTRACT FROM AUTHOR]