Online Sensorless Solar Power Forecasting for Microgrid Control and Automation

Meteorological conditions such as air density, temperature, solar radiation etc. strongly affect the power generation from solar, and thus, the prediction and estimation process should consider weather conditions as critical inputs. The nature of weather forecast is highly unpredictable, so many applications use meteorological data from in-place on-site sensors to add to the forecast and some use complex networks with complicated mapping. The in-situ sensor approach and dense mapping methods, however, present several drawbacks. First, the use of sensors give rise to extra operational, installation and maintenance cost. Second, it requires significant amount of time to capture and accumulate data for various occasions and scenarios, and in addition, sensor itself can be the cause of error measurements. The complex methods are computational inefficient and may present suboptimal convergence. This paper presents a sensorless solar output power forecasting based on historical weather (publicly available from met office) and PV data. The algorithm uses simple to implement neural networks with few neurons and hidden layers for its training and allows for day a head forecast. The proposed methodology presents a guideline on how to select the relevant data from weather and how it affects the accuracy and training time of neural network. The benefit of developed method is an improvement on the energy management, utilization and reliability of the microgrid

A Comparison Study on Power Consumption Predicting of the Electric Propulsion Vessel

In recent years, concern for the energy efficiency of vessels has increased due to the influence of environmental pollution. Electric propulsion systems have more energy flexibility than mechanical propulsion systems. So, it can improve power efficiency by using batteries. For the utilization of batteries, it is necessary to predict the power consumption of electric propulsion vessels. In addition, researchers are studying multivariate time-series data for prediction. In contrast preceded studies were non applicability to electric propulsion systems for power consumption predicting. Because, limitation of accessible to vessel's data and the previous prediction researches were considerably studied to small ranged electrical data. According to these reasons, The research of models that capable a wide range of vessel load data is essential. In this paper, aims to predict the electricity consumption of a vessel, using real vessel data and convert to electric propulsion vessel data, and select variables that affecting vessel's electricity consumption using heuristic. The converted data includes missing values, this can cause of weakens model's accuracy, therefore multiple imputation algorithm was used for cover it. After data preprocessing, several models are created to predict time-series data. This consists of single models for comparison criteria : LSTM(Long Short-term Memory models), CNN(Convolutional Neural Network), ANN(Artificial Neural Network), DNN(Deep Neural Network), bidirectional LSTM, and conjunction models : CNN-LSTM (direct), CNN-bidirectional LSTM (direct), CNN-LSTM (parallel), CNN- bidirectional LSTM (parallel). After models creation, the experiment method was decided, considered by clear comparison. that was composed of repeat test for the model's performance validation and utilized the widely used accuracy metric : RMSE.1. 서론 1 1.1 연구 배경 1 1.2 연구 동향 3 1.3 연구 내용 및 구성 6 2. 전기추진 선박 데이터 처리 기법 9 2.1 선박 운항 데이터 9 2.1.1 선박제원 9 2.1.2 수집 데이터 개요 10 2.1.3 데이터 변환 12 2.1.4 선박 운항 모드 분석 14 2.2 데이터 처리 16 2.2.1 데이터 분석 16 2.2.2 데이터 산출 및 보완 22 2.2.3 데이터 변환 23 3. 전기추진 선박 부하 예측 모델 설계 및 구현 27 3.1 이론적 배경 및 실험 절차 27 3.1.1 모델의 이론적 배경 27 3.1.2 예측 모델의 실험 절차 36 3.2 예측 모델 설계 및 구현 40 3.2.1 LSTM 41 3.2.2 bidirectional LSTM 42 3.2.3 CNN-LSTM (direct) 43 3.2.4 CNN-bidirectional LSTM (direct) 45 3.2.5 CNN-LSTM (parallel) 47 3.2.6 CNN-bidirectional LSTM (parallel) 49 3.2.7 LSTM auto encoder 51 3.2.8 ANN 53 3.2.9 DNN 54 3.2.10 CNN 55 4. 제안 모델 평가 58 4.1 모델 평가 기준 58 4.2 실험 환경 59 4.3 실험 결과 60 4.2.1 LSTM 60 4.2.2 bidirectional LSTM 62 4.2.3 CNN-LSTM (direct) 64 4.2.4 CNN-bidirectional LSTM (direct) 66 4.2.5 CNN-LSTM (parallel) 68 4.2.6 CNN-bidirectional LSTM (parallel) 70 4.2.7 LSTM auto encoder 72 4.2.8 ANN 74 4.2.9 DNN 76 4.2.10 CNN 78 5. 모델 비교 분석 및 고찰 80 5.1 모델 비교 분석 80 5.2 연구의 고찰 86 6. 결론 87 감사의 글 89 참고문헌 90Docto

Sensorless PV Power Forecasting in Grid-Connected Buildings through Deep Learning

Existing works in photovoltaic (PV) power generation focus on accurately predicting the PV power output on a forecast horizon. As the solar power generation is heavily influenced by meteorological conditions such as solar radiation, the weather forecast is a critical input in the prediction performance. However, the weather forecast is traditionally considered to have coarse granularity, so many are compelled to use on-site meteorological sensors to complement it. However, the approach involving on-site sensors has several issues. First, it incurs the cost in the installation, operation, and management of the sensors. Second, the physical model of the sensor dynamics itself can be a source of forecast errors. Third, it requires an accumulation of sensory data that represent all seasonal variations, which takes time to collect. In this paper, we take an alternative approach to use a relatively large deep neural network (DNN) instead of the on-site sensors to cope with the coarse-grained weather forecast. With historical PV output power data from our grid-connected building with a rooftop PV power generation facility and the publicly available weather forecast history data, we demonstrate that we can train a six-layer feedforward DNN for the day-ahead forecast. It achieves the average mean absolute error (MAE) of 2.9%, comparable to that of the conventional model, but without involing the on-site sensors