This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

/pdf/online-short-term-solar-power-forecasting-1ljgpbshkb.pdf

Online Short-term Solar Power Forecasting

Efficient Carbon Nanotube Photodiodes A single photon absorbed in a single-walled carbon nanotube device can generate multiple unbound particles carrying an electric charge. Gabor et al. (p. 1367) report that in such a device at low temperatures, excitation with light of increasing energy leads to well-defined stepwise increases in current. Interestingly, because of the unique band structure of carbon nanotubes, this behavior is analogous to particle-antiparticle creation commonly observed in high-energy particle physics. These observations point to the promise of investigations in other nanoscale carbon systems, such as graphene, and could lead to numerous applications, including highly sensitive photon detection and ultra-efficient photovoltaics. The decay of photoexcited electrons in a carbon nanotube device creates multiple pairs of charge carriers. We observed highly efficient generation of electron-hole pairs due to impact excitation in single-walled carbon nanotube p-n junction photodiodes. Optical excitation into the second electronic subband E22 leads to striking photocurrent steps in the device I-VSD characteristics that occur at voltage intervals of the band-gap energy EGAP/e. Spatially and spectrally resolved photocurrent combined with temperature-dependent studies suggest that these steps result from efficient generation of multiple electron-hole pairs from a single hot E22 carrier. This process is both of fundamental interest and relevant for applications in future ultra-efficient photovoltaic devices.

https://www.lassp.cornell.edu/lassp_data/mceuen/homepage/Publications/Gabor_Science_2009_supmat.pdf

Supporting Online Material for Extremely Efficient Multiple Electron-Hole Pair Generation in Carbon Nanotube Photodiodes

Prediction of solar energy guided by pearson correlation using machine learning

Assessment of the public acceptance and utilization of renewable energy in Pakistan

https://www.econstor.eu/bitstream/10419/244016/1/1693682400.pdf

Assessing the energy dynamics of Pakistan: Prospects of biomass energy

Pakistan is a developing economy with a mainstay shifting gradually from agriculture to industry. Improving lifestyles, population growth, and rapid urbanization have led to a continuous increase in the demand of energy. The demand-supply gap amounts to 5000 MW on average which reaches 7000 MW in the month of July when the energy demand is at peak. This shortfall causes 4%–7% loss to the gross domestic product. Energy demand is at a constant peak of 8%–10% per annum. Fossil fuels make 61% of energy mix, while the share of renewables is less than 1%. This situation calls for careful analysis and review of Pakistan's energy dynamics and to explore the potential of renewable energy resources. Biomass, a promising resource of renewable energy, is abundantly available in Pakistan. Biomass is found in various forms from firewood to crop residues to municipal solid waste. About 50% of domestic energy requirements are fulfilled by fuelwood and 34% by animal and crop residues. However, the availability of fuelwood reduces as the rate of growth of forests has shrunk to 8.8%. Other biomass resources like charcoal are used at the minor level. Bagasse produced by 70 sugar industries in the country has been found to be sufficient for the generation of 5700 GWh of electricity. Major crop residues include cotton stalks, wheat straw, rice straw, sugarcane trash, and corn stalk having production of 49.4, 34.581, 16.75, 7.83, and 5.325 million tons, respectively. Collective processing residues per annum of these crops amount to 25.271 million tons having a power generation potential of 689.25 TWh annually. Animal dung also offers encouraging potential of power generation. The total number of animals in the country is 51 million. The potential of electricity generation from animal dung at the national level has been estimated to be 4761 to 5554 MW. The municipal solid waste potential for production of energy by biochemical and thermochemical conversion is 216 kWh/t and 552 kWh/t, respectively. It was found that the country possesses potential of successfully running 15 million biogas plants. This review, which policymakers and researchers may find to be useful, revealed that biomass energy could help to achieve the target of increasing share of renewables in energy mix of the country from less than 1% to 5% by 2030 as envisaged by the Government of Pakistan.

Sketching Pakistan's energy dynamics: Prospects of biomass energy

Incorporation of carbon nanotubes in photoactive layer of organic solar cells

Solar energy has the property of alternating, fluctuation and periodicity, and it has severe impact on large scale photovoltaic (PV) grid-connected generation. This turn power utilities contrary to use PV power since the forecasting and overall assessment of the grid becomes very difficult. To develop a reliable algorithm that can minimize the errors associated with forecasting the nearby future PV power generation is particularly helpful for efficiently integration into the grid. PV power prediction can play a significant role in undertaking these challenges. This paper presents 3 days ahead power output forecasting of a PV system using a Theoretical Solar radiation and Elman Neural Network (ENN) software engineering technique by including the relations of PV power with solar radiation, temperature, humidity, and wind speed data. In the proposed method, the ENN is applied to have a significant effect on random PV power time-series data, and tackle the nonlinear fluctuations in a better approach.

Photovoltaic power forecasting based on Elman Neural Network software engineering method

High concentration of greenhouse gases in the atmosphere has increased dependency on photovoltaic (PV) power, but its random nature poses a challenge for system operators to precisely predict and forecast PV power. The conventional forecasting methods were accurate for clean weather. But when the PV plants worked under heavy haze, the radiation is negatively impacted and thus reducing PV power; therefore, to deal with haze weather, Air Quality Index (AQI) is introduced as a parameter to predict PV power. AQI, which is an indication of how polluted the air is, has been known to have a strong correlation with power generated by the PV panels. In this paper, a hybrid method based on the model of conventional back propagation (BP) neural network for clear weather and BP AQI model for haze weather is used to forecast PV power with conventional parameters like temperature, wind speed, humidity, solar radiation, and an extra parameter of AQI as input. The results show that the proposed method has less error under haze condition as compared to conventional model of neural network.

/pdf/hybrid-power-forecasting-model-for-photovoltaic-plants-based-22zwfa9ea3.pdf

Idris Khan

Papers

Sketching Pakistan's energy dynamics: Prospects of biomass energy

Incorporation of carbon nanotubes in photoactive layer of organic solar cells

Photovoltaic power forecasting based on Elman Neural Network software engineering method

Hybrid Power Forecasting Model for Photovoltaic Plants Based on Neural Network with Air Quality Index

A Review of the Challenges and Possibilities of Using Carbon Nanotubes in Organic Solar Cells