Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

Energy models for demand forecasting—A review

Accurate annual power load forecasting can provide reliable guidance for power grid operation and power construction planning, which is also important for the sustainable development of electric power industry. The annual power load forecasting is a non-linear problem because the load curve shows a non-linear characteristic. Generalized regression neural network (GRNN) has been proven to be effective in dealing with the non-linear problems, but it is very regretfully finds that the GRNN have rarely been applied to the annual power load forecasting. Therefore, the GRNN was used for annual power load forecasting in this paper. However, how to determine the appropriate spread parameter in using the GRNN for power load forecasting is a key point. In this paper, a hybrid annual power load forecasting model combining fruit fly optimization algorithm (FOA) and generalized regression neural network was proposed to solve this problem, where the FOA was used to automatically select the appropriate spread parameter value for the GRNN power load forecasting model. The effectiveness of this proposed hybrid model was proved by two experiment simulations, which both show that the proposed hybrid model outperforms the GRNN model with default parameter, GRNN model with particle swarm optimization (PSOGRNN), least squares support vector machine with simulated annealing algorithm (SALSSVM), and the ordinary least squares linear regression (OLS_LR) forecasting models in the annual power load forecasting.

A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm

https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1063&context=dubenart

A clustering approach to domestic electricity load profile characterisation using smart metering data

https://ir.nctu.edu.tw:443/bitstream/11536/16058/1/000303146000039.pdf

Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model

Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods

Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

The intensity of ultraviolet (UV) radiation affects the yield and quality of red lettuce. The current study aimed to develop a UV management system in a greenhouse to achieve high yield and quality in red lettuce production. The study consisted of two experiments. In the first experiment, the effects of the different UV transparencies of the plastic materials covering the greenhouse on plant growth and the concentration of antioxidants in red lettuce were studied. For this purpose, two greenhouses were covered with polyethene of different transparencies to UV radiation. One greenhouse was covered with a common type of polyethene transparent in a large spectrum of UV radiation (UV-open), while the second greenhouse was covered with polyethene untransparent to ultraviolet radiation (UV-block). The plants were grown in a deep flotation hydroponic system. At the end of the cultivation, plant growth measurements, leaf colour measurements, and the determination of antioxidant components’ concentration were carried out. Red lettuce plants harvested 42 days after planting had an average head weight 42% greater in the UV-block greenhouse compared to plants grown in the UV-open greenhouse. However, the red leaf colour of plants in the UV-block greenhouse lagged significantly compared to that in the UV-open greenhouse. Moreover, the total phenolic content, the total flavonoid content, and the antioxidant capacity of the lettuce leaves in the UV-block greenhouse were significantly lower compared to the corresponding values of the plants in the UV-open greenhouse. During the second experiment, a new cultivation system of red lettuce, which combined a UV-block polyethene film as a greenhouse cover and a pre-harvested supplemental UV light, was tested. For this purpose, various doses of supplemental UV lighting were tested in the UV-block greenhouse for ten days prior to harvest. From these tests, it emerged that applying supplemental UV lighting with a dose of 425 kJ m−2 d−1 for ten days before harvest produces red lettuces of the same quality as those produced in a UV-open greenhouse. This technique of growing red lettuce increases its yield by 30% without a negative effect on the quality of the product.

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Ultraviolet Radiation Management in Greenhouse to Improve Red Lettuce Quality and Yield

The Hellenic electrical distribution lines have been basically supported with the use of wood poles. Outages due to aging, degradation, or broken wood poles may infer significant costs due to repair and loss of energy to consumers. Lifetime estimation of wood poles is significant for maintenance and replacement decisions at the power utility. The paper presents a methodology, based on a statistical analysis, for the evaluation of the life expectancy and the production of a survival curve of a typical Hellenic distribution wood pole in-service. The proposed methodology intends to minimize the life-cycle cost of inspection and refurbishment of wood poles in the distribution network of Hellas, something that is really important in the new competitive and liberating Hellenic electrical energy market.

D. Ch. Karamousantas

Papers

Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

Ultraviolet Radiation Management in Greenhouse to Improve Red Lettuce Quality and Yield

Life Expectancy Evaluation of the Wood Poles Which Support the Hellenic Electrical Distribution Network